Load data

Internal

CN

Read raw

Templates

Merge raw w/ template

14C

Mass


Call:
lm(formula = loss_pct ~ PM + ECO, data = sra.frc.mss.lss.df[-which(sra.frc.mss.lss.df$Probe == 
    "BSpp_comp_2001_18-28"), ])

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1953 -0.9261 -0.4445  0.7965  5.7767 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   4.8136     0.5726   8.406 1.06e-10 ***
PMBS         -0.1405     0.6486  -0.217 0.829539    
PMGR         -2.3959     0.6382  -3.754 0.000507 ***
ECOwf        -1.0117     0.6382  -1.585 0.120098    
ECOrf        -1.7850     0.6394  -2.792 0.007729 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.831 on 44 degrees of freedom
Multiple R-squared:  0.3641,    Adjusted R-squared:  0.3063 
F-statistic: 6.299 on 4 and 44 DF,  p-value: 0.0004255

Interestingly, it appears that AN soils have proportionally LESS minC by mass than do GR or BS soils, significantly so at depth. Why would this be? Possibly because AN soils have higher losses (DOC) during fractionation?

External data

Atmosphere

from C. Rasmussen (’01 C, N; ’09 14C, frc mass, C, N)

calculating Δ14C from fraction modern

Analysis

Misc. functions

Bulk C

2001

2009

2019

`summarise()` has grouped output by 'PMeco'. You can override using the `.groups` argument.

Fraction C

C, CN profiles

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C distribution

Error in rbind(deparse.level, ...) : 
  numbers of columns of arguments do not match

Fraction 14C

Depth profiles

Spline ’01, ’09

Spline C

converting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to character
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[[2]]

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Spline 14C

C-weighted 14C spline

14C time series

$`10`

$`20`

$`30`

$`10`

$`20`

$`30`

$emtrends
 PMeco year.trend   SE df lower.CL upper.CL
 ANpp      -6.795 2.07  9   -11.47    -2.12
 ANrf      -1.018 2.07  9    -5.70     3.66
 ANwf      -0.681 2.07  9    -5.36     4.00
 BSpp      -1.842 2.07  9    -6.52     2.84
 BSrf      -2.766 2.07  9    -7.44     1.91
 BSwf      -1.453 2.07  9    -6.13     3.23
 GRpp      -4.189 2.07  9    -8.87     0.49
 GRrf      -0.779 2.07  9    -5.46     3.90
 GRwf      -2.078 2.07  9    -6.76     2.60

Confidence level used: 0.95 

$contrasts
 contrast    estimate   SE df t.ratio p.value
 ANpp - ANrf  -5.7768 2.92  9 -1.975  0.5893 
 ANpp - ANwf  -6.1137 2.92  9 -2.090  0.5287 
 ANpp - BSpp  -4.9532 2.92  9 -1.694  0.7388 
 ANpp - BSrf  -4.0290 2.92  9 -1.378  0.8815 
 ANpp - BSwf  -5.3424 2.92  9 -1.827  0.6689 
 ANpp - GRpp  -2.6063 2.92  9 -0.891  0.9879 
 ANpp - GRrf  -6.0161 2.92  9 -2.057  0.5460 
 ANpp - GRwf  -4.7170 2.92  9 -1.613  0.7792 
 ANrf - ANwf  -0.3370 2.92  9 -0.115  1.0000 
 ANrf - BSpp   0.8235 2.92  9  0.282  1.0000 
 ANrf - BSrf   1.7477 2.92  9  0.598  0.9991 
 ANrf - BSwf   0.4344 2.92  9  0.149  1.0000 
 ANrf - GRpp   3.1704 2.92  9  1.084  0.9630 
 ANrf - GRrf  -0.2394 2.92  9 -0.082  1.0000 
 ANrf - GRwf   1.0598 2.92  9  0.362  1.0000 
 ANwf - BSpp   1.1605 2.92  9  0.397  1.0000 
 ANwf - BSrf   2.0847 2.92  9  0.713  0.9971 
 ANwf - BSwf   0.7714 2.92  9  0.264  1.0000 
 ANwf - GRpp   3.5074 2.92  9  1.199  0.9377 
 ANwf - GRrf   0.0976 2.92  9  0.033  1.0000 
 ANwf - GRwf   1.3967 2.92  9  0.478  0.9998 
 BSpp - BSrf   0.9242 2.92  9  0.316  1.0000 
 BSpp - BSwf  -0.3891 2.92  9 -0.133  1.0000 
 BSpp - GRpp   2.3469 2.92  9  0.802  0.9937 
 BSpp - GRrf  -1.0629 2.92  9 -0.363  1.0000 
 BSpp - GRwf   0.2362 2.92  9  0.081  1.0000 
 BSrf - BSwf  -1.3133 2.92  9 -0.449  0.9999 
 BSrf - GRpp   1.4227 2.92  9  0.486  0.9998 
 BSrf - GRrf  -1.9871 2.92  9 -0.679  0.9979 
 BSrf - GRwf  -0.6880 2.92  9 -0.235  1.0000 
 BSwf - GRpp   2.7360 2.92  9  0.935  0.9838 
 BSwf - GRrf  -0.6737 2.92  9 -0.230  1.0000 
 BSwf - GRwf   0.6254 2.92  9  0.214  1.0000 
 GRpp - GRrf  -3.4098 2.92  9 -1.166  0.9459 
 GRpp - GRwf  -2.1107 2.92  9 -0.722  0.9968 
 GRrf - GRwf   1.2991 2.92  9  0.444  0.9999 

P value adjustment: tukey method for comparing a family of 9 estimates 

Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "fPOM" & dens.01.09.19.sp.df$lyr_bot == 10, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-36.987  -8.459  -3.794  14.059  31.963 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)               165.512     25.131   6.586 0.000101 ***
year_i                     -6.795      2.068  -3.286 0.009443 ** 
pmbasalt                  -43.636     35.541  -1.228 0.250674    
pmgranite                 -23.223     35.541  -0.653 0.529818    
ecocool                  -168.432     35.541  -4.739 0.001060 ** 
ecocold                  -104.629     35.541  -2.944 0.016382 *  
year_i:pmbasalt             4.953      2.925   1.694 0.124591    
year_i:pmgranite            2.606      2.925   0.891 0.396052    
year_i:ecocool              6.114      2.925   2.090 0.066144 .  
year_i:ecocold              5.777      2.925   1.975 0.079678 .  
pmbasalt:ecocool           93.779     50.262   1.866 0.094932 .  
pmgranite:ecocool         103.988     50.262   2.069 0.068483 .  
pmbasalt:ecocold           62.329     50.262   1.240 0.246303    
pmgranite:ecocold          17.972     50.262   0.358 0.728903    
year_i:pmbasalt:ecocool    -5.725      4.136  -1.384 0.199702    
year_i:pmgranite:ecocool   -4.003      4.136  -0.968 0.358422    
year_i:pmbasalt:ecocold    -6.701      4.136  -1.620 0.139664    
year_i:pmgranite:ecocold   -2.367      4.136  -0.572 0.581154    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.38 on 9 degrees of freedom
Multiple R-squared:  0.8821,    Adjusted R-squared:  0.6595 
F-statistic: 3.962 on 17 and 9 DF,  p-value: 0.02039


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "fPOM" & dens.01.09.19.sp.df$lyr_bot == 20, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-26.206  -9.681  -4.089   6.602  47.172 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)                78.039     28.446   2.743   0.0227 *
year_i                     -4.217      2.341  -1.801   0.1052  
pmbasalt                    2.083     40.229   0.052   0.9598  
pmgranite                 -34.956     40.229  -0.869   0.4075  
ecocool                  -127.141     40.229  -3.160   0.0115 *
ecocold                   -68.688     40.229  -1.707   0.1219  
year_i:pmbasalt             3.623      3.311   1.094   0.3022  
year_i:pmgranite            8.217      3.311   2.482   0.0349 *
year_i:ecocool              4.120      3.311   1.245   0.2447  
year_i:ecocold              3.572      3.311   1.079   0.3086  
pmbasalt:ecocool           71.432     56.892   1.256   0.2409  
pmgranite:ecocool         113.279     56.892   1.991   0.0777 .
pmbasalt:ecocold           47.147     56.892   0.829   0.4287  
pmgranite:ecocold          37.800     56.892   0.664   0.5231  
year_i:pmbasalt:ecocool    -6.787      4.682  -1.450   0.1811  
year_i:pmgranite:ecocool  -10.673      4.682  -2.280   0.0486 *
year_i:pmbasalt:ecocold    -5.429      4.682  -1.160   0.2761  
year_i:pmgranite:ecocold   -8.132      4.682  -1.737   0.1164  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 29.86 on 9 degrees of freedom
Multiple R-squared:  0.8625,    Adjusted R-squared:  0.6027 
F-statistic:  3.32 on 17 and 9 DF,  p-value: 0.03583


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "fPOM" & dens.01.09.19.sp.df$lyr_bot == 30, ])

Residuals:
   Min     1Q Median     3Q    Max 
-32.57 -11.84  -3.65   7.70  58.63 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)
(Intercept)               -7.7862    34.5553  -0.225    0.827
year_i                    -0.5033     2.8436  -0.177    0.863
pmbasalt                  57.2270    48.8686   1.171    0.272
pmgranite                 19.0016    48.8686   0.389    0.706
ecocool                  -67.2614    48.8686  -1.376    0.202
ecocold                  -27.5273    48.8686  -0.563    0.587
year_i:pmbasalt            0.5886     4.0215   0.146    0.887
year_i:pmgranite           3.3000     4.0215   0.821    0.433
year_i:ecocool             0.9728     4.0215   0.242    0.814
year_i:ecocold             1.3306     4.0215   0.331    0.748
pmbasalt:ecocool          45.7950    69.1106   0.663    0.524
pmgranite:ecocool         65.6280    69.1106   0.950    0.367
pmbasalt:ecocold          -1.7179    69.1106  -0.025    0.981
pmgranite:ecocold          3.6995    69.1106   0.054    0.958
year_i:pmbasalt:ecocool   -7.3011     5.6873  -1.284    0.231
year_i:pmgranite:ecocool  -5.8728     5.6873  -1.033    0.329
year_i:pmbasalt:ecocold   -2.4287     5.6873  -0.427    0.679
year_i:pmgranite:ecocold  -3.7023     5.6873  -0.651    0.531

Residual standard error: 36.27 on 9 degrees of freedom
Multiple R-squared:  0.776, Adjusted R-squared:  0.353 
F-statistic: 1.835 on 17 and 9 DF,  p-value: 0.178


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "minC" & dens.01.09.19.sp.df$lyr_bot == 10, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-32.445 -14.690  -0.982   8.313  49.666 

Coefficients:
                            Estimate  Std. Error t value Pr(>|t|)  
(Intercept)                53.235715   31.836582   1.672   0.1288  
year_i                     -1.368463    2.619903  -0.522   0.6141  
pmbasalt                  -11.392863   45.023726  -0.253   0.8059  
pmgranite                  13.808110   45.023726   0.307   0.7661  
ecocool                  -129.550490   45.023726  -2.877   0.0183 *
ecocold                   -53.252323   45.023726  -1.183   0.2672  
year_i:pmbasalt             1.011690    3.705102   0.273   0.7910  
year_i:pmgranite           -0.008576    3.705102  -0.002   0.9982  
year_i:ecocool              3.341545    3.705102   0.902   0.3906  
year_i:ecocold             -0.800799    3.705102  -0.216   0.8337  
pmbasalt:ecocool          100.351702   63.673164   1.576   0.1495  
pmgranite:ecocool         101.017634   63.673164   1.587   0.1471  
pmbasalt:ecocold           -2.086764   63.673164  -0.033   0.9746  
pmgranite:ecocold         -45.174314   63.673164  -0.709   0.4960  
year_i:pmbasalt:ecocool    -2.576587    5.239806  -0.492   0.6347  
year_i:pmgranite:ecocool   -3.747488    5.239806  -0.715   0.4926  
year_i:pmbasalt:ecocold     2.650378    5.239806   0.506   0.6251  
year_i:pmgranite:ecocold    4.370745    5.239806   0.834   0.4258  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 33.41 on 9 degrees of freedom
Multiple R-squared:  0.7665,    Adjusted R-squared:  0.3254 
F-statistic: 1.738 on 17 and 9 DF,  p-value: 0.2008


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "minC" & dens.01.09.19.sp.df$lyr_bot == 20, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-25.918  -9.446  -2.474   9.149  31.546 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -9.5997    21.9478  -0.437   0.6721  
year_i                     0.7565     1.8061   0.419   0.6851  
pmbasalt                  -8.2247    31.0389  -0.265   0.7970  
pmgranite                -11.4347    31.0389  -0.368   0.7211  
ecocool                  -94.7296    31.0389  -3.052   0.0137 *
ecocold                  -15.3565    31.0389  -0.495   0.6326  
year_i:pmbasalt            0.7698     2.5543   0.301   0.7700  
year_i:pmgranite           3.0074     2.5543   1.177   0.2692  
year_i:ecocool             1.2303     2.5543   0.482   0.6415  
year_i:ecocold            -2.6857     2.5543  -1.051   0.3205  
pmbasalt:ecocool         127.3757    43.8956   2.902   0.0175 *
pmgranite:ecocool        124.8421    43.8956   2.844   0.0193 *
pmbasalt:ecocold          -0.9729    43.8956  -0.022   0.9828  
pmgranite:ecocold        -31.2191    43.8956  -0.711   0.4950  
year_i:pmbasalt:ecocool   -4.6467     3.6123  -1.286   0.2304  
year_i:pmgranite:ecocool  -5.8824     3.6123  -1.628   0.1379  
year_i:pmbasalt:ecocold    2.9547     3.6123   0.818   0.4345  
year_i:pmgranite:ecocold   2.1864     3.6123   0.605   0.5599  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.04 on 9 degrees of freedom
Multiple R-squared:  0.8619,    Adjusted R-squared:  0.6011 
F-statistic: 3.305 on 17 and 9 DF,  p-value: 0.03635


Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == 
    "minC" & dens.01.09.19.sp.df$lyr_bot == 30, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-21.580  -9.094   3.950   6.546  38.845 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -54.2864    20.6763  -2.626   0.0276 *
year_i                     1.7628     1.7015   1.036   0.3272  
pmbasalt                  -4.1368    29.2407  -0.141   0.8906  
pmgranite                -39.1905    29.2407  -1.340   0.2130  
ecocool                  -58.1383    29.2407  -1.988   0.0780 .
ecocold                   15.4194    29.2407   0.527   0.6107  
year_i:pmbasalt           -0.1398     2.4063  -0.058   0.9550  
year_i:pmgranite           3.6500     2.4063   1.517   0.1636  
year_i:ecocool            -1.0213     2.4063  -0.424   0.6812  
year_i:ecocold            -5.3002     2.4063  -2.203   0.0551 .
pmbasalt:ecocool         132.3396    41.3525   3.200   0.0108 *
pmgranite:ecocool        133.4644    41.3525   3.227   0.0104 *
pmbasalt:ecocold         -34.7870    41.3525  -0.841   0.4220  
pmgranite:ecocold         -6.5936    41.3525  -0.159   0.8768  
year_i:pmbasalt:ecocool   -4.8607     3.4030  -1.428   0.1870  
year_i:pmgranite:ecocool  -4.8928     3.4030  -1.438   0.1843  
year_i:pmbasalt:ecocold    6.4371     3.4030   1.892   0.0911 .
year_i:pmgranite:ecocold   3.3074     3.4030   0.972   0.3565  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.7 on 9 degrees of freedom
Multiple R-squared:  0.8748,    Adjusted R-squared:  0.6383 
F-statistic: 3.699 on 17 and 9 DF,  p-value: 0.0255
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions

Char pool calc

Fraction vs respired 14C


Call:
lm(formula = fPOM ~ d14c * pm * eco, data = dens.inc.reps.w)

Residuals:
    Min      1Q  Median      3Q     Max 
-59.479 -10.962  -2.964  13.622  56.625 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)   
(Intercept)            -46.8010    16.9411  -2.763  0.00725 **
d14c                     1.0008     0.2921   3.426  0.00101 **
pmbasalt                43.3810    24.1340   1.798  0.07639 . 
pmgranite               43.0356    19.5950   2.196  0.03125 * 
ecocool                -38.8844    37.2750  -1.043  0.30031   
ecowarm                 15.4182    19.7989   0.779  0.43865   
d14c:pmbasalt           -0.4179     0.3512  -1.190  0.23791   
d14c:pmgranite          -0.2566     0.3554  -0.722  0.47254   
d14c:ecocool            -0.4688     0.5535  -0.847  0.39982   
d14c:ecowarm             0.1097     0.3147   0.349  0.72834   
pmbasalt:ecocool       -14.2091    41.9264  -0.339  0.73565   
pmgranite:ecocool       28.0420    39.6631   0.707  0.48181   
pmbasalt:ecowarm        26.1699    29.2181   0.896  0.37337   
pmgranite:ecowarm       15.1343    24.9114   0.608  0.54539   
d14c:pmbasalt:ecocool    0.7267     0.5999   1.211  0.22967   
d14c:pmgranite:ecocool   0.5756     0.5987   0.961  0.33951   
d14c:pmbasalt:ecowarm   -0.1299     0.4082  -0.318  0.75112   
d14c:pmgranite:ecowarm  -0.1381     0.3939  -0.350  0.72699   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 22.62 on 73 degrees of freedom
Multiple R-squared:  0.8853,    Adjusted R-squared:  0.8586 
F-statistic: 33.14 on 17 and 73 DF,  p-value: < 2.2e-16


Call:
lm(formula = minC ~ d14c * pm * eco + year, data = dens.inc.reps.w)

Residuals:
   Min     1Q Median     3Q    Max 
-73.03 -10.91   2.62  13.50  57.87 

Coefficients:
                        Estimate Std. Error t value      Pr(>|t|)    
(Intercept)            -121.9498    17.6236  -6.920 0.00000000155 ***
d14c                      0.7669     0.2857   2.684      0.009017 ** 
pmbasalt                111.7400    23.6361   4.728 0.00001098055 ***
pmgranite                88.6981    19.0431   4.658 0.00001425529 ***
ecocool                  -8.2650    36.4407  -0.227      0.821217    
ecowarm                  82.1645    19.2602   4.266 0.00005960539 ***
year2019                 20.4426     6.0751   3.365      0.001230 ** 
d14c:pmbasalt            -0.8841     0.3444  -2.568      0.012318 *  
d14c:pmgranite           -0.5648     0.3455  -1.635      0.106480    
d14c:ecocool             -0.4567     0.5401  -0.846      0.400524    
d14c:ecowarm             -0.2215     0.3056  -0.725      0.470966    
pmbasalt:ecocool        -34.0345    41.5389  -0.819      0.415296    
pmgranite:ecocool        17.2802    38.8323   0.445      0.657658    
pmbasalt:ecowarm       -131.4919    28.5973  -4.598 0.00001779658 ***
pmgranite:ecowarm       -83.9280    24.2075  -3.467      0.000892 ***
d14c:pmbasalt:ecocool     1.1027     0.5893   1.871      0.065396 .  
d14c:pmgranite:ecocool    0.6565     0.5863   1.120      0.266566    
d14c:pmbasalt:ecowarm     1.2436     0.4000   3.109      0.002689 ** 
d14c:pmgranite:ecowarm    0.7410     0.3852   1.924      0.058362 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.96 on 72 degrees of freedom
Multiple R-squared:  0.8299,    Adjusted R-squared:  0.7874 
F-statistic: 19.52 on 18 and 72 DF,  p-value: < 2.2e-16
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions

Thermal fractions

C release (thermograms)

Thermal fraction 14C

Currently analysing dataframe 
Between 0 and 140 degrees C the proportion of sample C is 0.001488078 
Between 140 and 245 degrees C the proportion of sample C is 0.03870194 
Between 245 and 340 degrees C the proportion of sample C is 0.3571529 
Between 340 and 390 degrees C the proportion of sample C is 0.2355014 
Between 390 and 495 degrees C the proportion of sample C is 0.2936209 
Between 495 and 791 degrees C the proportion of sample C is 0.07359725 
Currently analysing dataframe 
Between 0 and 141.5 degrees C the proportion of sample C is 0.002647777 
Between 141.5 and 246.5 degrees C the proportion of sample C is 0.07616599 
Between 246.5 and 290 degrees C the proportion of sample C is 0.1643875 
Between 290 and 364 degrees C the proportion of sample C is 0.3792163 
Between 364 and 484 degrees C the proportion of sample C is 0.3074933 
Between 484 and 788 degrees C the proportion of sample C is 0.07006177 
Currently analysing dataframe 
Between 0 and 143 degrees C the proportion of sample C is 0.005167379 
Between 143 and 248 degrees C the proportion of sample C is 0.07075111 
Between 248 and 293 degrees C the proportion of sample C is 0.163502 
Between 293 and 367 degrees C the proportion of sample C is 0.3797734 
Between 367 and 487 degrees C the proportion of sample C is 0.3038745 
Between 487 and 792 degrees C the proportion of sample C is 0.07718191 

---
title: "Sierra Nevada Fraction Analysis"
author: J. Beem-Miller
date: \textit{\today}
output:
  html_document:
    df_print: paged
    toc: yes
    toc_depth: '2'
  pdf_document:
    latex_engine: xelatex
    toc: yes
    toc_depth: 3
  html_notebook:
    css: "custom.css"
    toc: yes
    toc_depth: 2
header_includes:
- \usepackage[utf8]{inputenc}
- \usepackage{float}
---

```{r global_options, include = FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE,
                      fig.align = 'center', dev = c('cairo_pdf', 'png'), fig.width = 6.5,
                      fig.height = 3.5)
options(scipen = 5)
# load page breaks fx
source("./utilities/page_break_Rmd.R")
```

```{r setup, include = FALSE}
library(ggplot2)
library(dplyr)
library(tidyr)
library(SoilR)
library(openxlsx)
library(ISRaD)
library(lme4)
library(lmerTest)
library(emmeans)
library(gt)
library(scales)
library(GSIF)
library(aqp)
```

```{r load raw-data-ingest fx}
source("./utilities/jena_ams_ingest.R")
source("./utilities/jena_iso_ingest.R")
source("./utilities/jena_elm_ingest.R")
```

```{r plot-funs}
# color palettes for ECO & PM
warm <- "#BF812D"
cool <- "#80CDC1"
cold <- "#01665E"
granite <- "#9daba9"
andesite <- "#382dbf"
basalt <- "#bf382d"
minC <- "#9b003f"
fPOM <- "#3f9b00"
oPOM <- "#0047af"
```

# Load data
## Internal 
### CN
#### Read raw

```{r load-cn-dat}
# 2001 & 2019 density fraction data
elm_results_dir <- list.files("../data/raw", pattern = "elm_jena_results", full.names = TRUE)
elm_results_ls <- lapply(seq_along(elm_results_dir), function(i) {
  if (grepl("elm_jena_results-soil", elm_results_dir[i])) {
    template_file <- "../data/raw/elm_jena_template/elm_jena_template2.xls"
  } else {
    template_file <- "../data/raw/elm_jena_template/elm_jena_template.xls"
  }
  read_jena_elm_results(elm_results_dir[i], template_file = template_file)
})
names(elm_results_ls) <- list.files("../data/raw", pattern = "elm_jena_results")

# separate bulkC from frcC
elm_results_blkC_ls <- elm_results_ls[which(grepl("elm_jena_results-soil", names(elm_results_ls)))]
elm_results_frcC_ls <- elm_results_ls[which(grepl("elm_jena_results-frc", names(elm_results_ls)))]

# extract bulk C and summarize
sra.blk.2019.df <- bind_rows(unlist(elm_results_blkC_ls, recursive = FALSE)) %>%
  mutate(PMeco = sapply(strsplit(ID, "_"), "[", 2),
         depth = sapply(strsplit(ID, "_"), "[", 4))
sra.blk.2019.sum.df <- sra.blk.2019.df %>%
  group_by(PMeco, depth) %>%
  summarize(across(c(C, N), .fns = list(mean = mean, sd = sd)), .groups = "drop") %>%
  mutate(ID2 = paste(PMeco, depth, sep = "_"))
```

#### Templates

```{r create-frc-data-templates}
## Create template for composite soil data (e.g. density fractions)
# Basic list template
PMeco.ls <- vector(mode = "list", length = 9)
names(PMeco.ls) <- levels(interaction(c("AN", "BS", "GR"), c("pp", "wf", "rf"), sep = ""))

# list of depths for 2001 samples
depth_bot_2001.ls <- list(ANpp = c(6, 13, 33),
                          ANwf = c(11, 35),
                          ANrf = c(11, 32),
                          BSpp = c(7, 18, 28),
                          BSwf = c(10, 19),
                          BSrf = c(8, 15, 30),
                          GRpp = c(7, 15, 27),
                          GRwf = c(4, 13, 28),
                          GRrf = c(8, 27))

# list of depths for 2019 samples
depth_bot_2019.ls <- lapply(PMeco.ls, function(df) seq(10, 30, 10))

# template fx
sra.frc.template.fx <- function(depth_bot, year) {
  nms <- names(depth_bot)
  ls <- lapply(seq_along(depth_bot), function(i) {
    n <- length(depth_bot[[i]])
    df <- data.frame(year = rep(year, n),
                     PM = rep(substr(nms[i], 1, 2), n), 
                     ECO = rep(substr(nms[i], 3, 4), n),
                     lyr_bot = depth_bot[[i]])
    df$lyr_top <- sapply(seq_along(depth_bot[[i]]), function(j) {
      if (j == 1) {
        0
      } else {
        depth_bot[[i]][j - 1]
      }
    })
    df$ID <- paste0(df$PM, df$ECO, "_comp_", df$year, "_", df$lyr_top, "-", df$lyr_bot)
    return(df)
  })
  names(ls) <- nms
  frc.ls <- replicate(3, ls, FALSE)
  names(frc.ls) <- c("FPOM", "OPOM", "MOM")
  return(frc.ls)
}

# 2001
sra.frc.tmp.2001.ls <- sra.frc.template.fx(depth_bot_2001.ls, 2001)

# 2019
sra.frc.tmp.2019.ls <- sra.frc.template.fx(depth_bot_2019.ls, 2019)
```

#### Merge raw w/ template

```{r fill-frc-cn}
# define extraction function
fill.cn.fx <- function(template, elm_results_ls, year, type) {
  
  # internal fx for extracting cn data and averaging analytical duplicates as needed
  extract.elm.fx <- function(ls) {
    lapply(ls, function(x) {
      ix <- grep(x$ID, cn.df$ID)
      if (length(ix > 1)) {
        x <- cbind(x,
                   C = mean(cn.df[ix, "C"]), 
                   N = mean(cn.df[ix, "N"]), 
                   row.names = NULL)
      } else if (length(ix) == 1) {
        x <- cbind(x, cn.df[ix, c("C", "N")], row.names = NULL)
      }
      return(x)
    })    
  }
  
  # select CN data by year
  cn.ls <- unlist(
    lapply(grep(paste0(type, substr(year, 3, 4)), names(elm_results_ls)), function(i) {
      elm_results_ls[[i]]
    }), recursive = FALSE)
  
  # store fraction names
  nms <- names(template)
  
  # loop for running extraction
  for(i in seq_along(nms)) {
    
    # make data frame of data for target fraction and year
    cn.df <- bind_rows(cn.ls[grep(names(template)[i], names(cn.ls))])
    
    # check if target fraction data exist and run extraction function if so
    if (nrow(cn.df) != 0) {
      
      # extract data for each fraction
      template[[i]] <- lapply(template[[i]], function(df) {
        bind_rows(extract.elm.fx(split(df, df$ID)))
      })
    }
  }
  
  # return output list named by target fraction
  names(template) <- nms
  return(template) 
}

# extract layer data by year
sra.frc.2001.ls <- fill.cn.fx(sra.frc.tmp.2001.ls, elm_results_frcC_ls, 2001, "frc")
sra.frc.2019.ls <- fill.cn.fx(sra.frc.tmp.2019.ls, elm_results_frcC_ls, 2019, "frc")
```

### 14C

```{r load-14c-dat}
# read ams dir
ams_jena_results_dirs <- list.files("../data/raw", pattern = "ams_jena_results", full.names = TRUE)

# list files w/ new and old templates by date
dates <- sapply(lapply(strsplit(ams_jena_results_dirs, "_(?!.*_)", perl = TRUE), "[[", 2), as.Date)
ams_jena_results_dirs_new <- ams_jena_results_dirs[which(dates > as.Date("2022-01-23"))]
ams_jena_results_dirs_old <- ams_jena_results_dirs[which(dates < as.Date("2022-01-23"))]

# new template
new_template <- "../data/raw/ams_jena_template_2022-01-24/ams_jena_template.xlsx"

# read in data
ams_results_ls <- c(
  lapply(seq_along(ams_jena_results_dirs_new), function(i) {
    read_jena_ams_results(ams_jena_results_dirs_new[i], 
                          template_file = new_template,
                          start = 31)
  }),
  lapply(seq_along(ams_jena_results_dirs_old), function(i) {
    read_jena_ams_results(ams_jena_results_dirs_old[i])
  }))
names(ams_results_ls) <- unlist(
  c(lapply(strsplit(ams_jena_results_dirs_new, "/(?!.*/)", perl = TRUE), "[[", 2),
    lapply(strsplit(ams_jena_results_dirs_old, "/(?!.*/)", perl = TRUE), "[[", 2)))

# remove flawed ANrf samples
ANrf.rm <- c("10_ANrf_comp_2019_0-10_MOM", 
             "11_ANrf_comp_2019_10-20_MOM_a",
             "12_ANrf_comp_2019_20-30_MOM")
if (any(!is.na(match(ANrf.rm, ams_results_ls$`ams_jena_results-frc19-MOM_2021-06-26`$`Beem-Miller_24.xlsx`$Probe)))) {
  ams_results_ls$`ams_jena_results-frc19-MOM_2021-06-26`$`Beem-Miller_24.xlsx` <- ams_results_ls$`ams_jena_results-frc19-MOM_2021-06-26`$`Beem-Miller_24.xlsx`[-match(ANrf.rm, ams_results_ls$`ams_jena_results-frc19-MOM_2021-06-26`$`Beem-Miller_24.xlsx`$Probe), ]
}

# # separate redos
# ams_results_redo_ls <- lapply(ams_results_ls, function(ls)
#     Filter(Negate(is.null), lapply(ls, function(df) {
#       df_R <- df[grep("_R", df$Probe), ] 
#       if (nrow(df_R) != 0) {
#         df_R
#       }
#     })))
# ams_results_redo_df <- bind_rows(Filter(
#     Negate(is.null), lapply(ams_results_redo_ls, function(ls) {
#       if (length(ls) > 0) {
#         bind_rows(ls)
#       }
#     }))) %>%
#   filter(!is.na(F14C))
```

### Mass

```{r load-frc-mass-data}
# load raw data
sra.frc.mss.raw <- read_excel("../data/raw/lab_jena_results-frc19-frc01_2021-05-05/Dichtefraktionierung_Jeff_2020.xls", sheet = "Tabelle1")

# filter and reduce
sra.frc.mss.df <- sra.frc.mss.raw %>%
  filter(is.na(SampleRedone)) %>%
  select(Probe, `Einwaage (g)`, Fraktion, `LF (g)`, `HF (g)`) %>%
  mutate(yield = ifelse(is.na(`LF (g)`), `HF (g)`, `LF (g)`)) %>%
  select(-c(`LF (g)`, `HF (g)`)) %>%
  rename(wt_g = `Einwaage (g)`)

# 1) combine multiple flask samples for individual flasks
# 2) fill in missing oPOM data from 2019 GRrf sites
# NB: almost no loss for 2001 sites, so seems justified to assume none in 2019
sra.frc.mss.wide.df <- bind_rows(
  lapply(split(sra.frc.mss.df, sra.frc.mss.df$Probe), function(df) {
    if (nrow(df) > 3) {
      df <- df %>% 
        group_by(Fraktion, Probe, wt_g) %>%
        summarize(yield = sum(yield), .groups = "drop")
    }
    return(df)
  })) %>%
  pivot_wider(names_from = Fraktion, values_from = yield) %>%
  rename(FPOM = fPOM, OPOM = oPOM, MOM = HF) %>%
  mutate(OPOM = ifelse(is.na(OPOM), wt_g - FPOM - MOM, OPOM))

# sum mass
sra.frc.mss.wide.df$mass_sum <- rowSums(sra.frc.mss.wide.df[ , c("FPOM", "OPOM", "MOM")])

# pivot longer and calculate mass pct
sra.frc.mss.long.df <- sra.frc.mss.wide.df %>%
  pivot_longer(cols = c("FPOM", "OPOM", "MOM"), names_to = "frc", values_to = "mass_g") %>%
  mutate(mass_pct = round(mass_g / mass_sum * 100, 1),
         depth = sapply(strsplit(Probe, "_(?!.*_)", perl = TRUE), "[[", 2),
         lyr_top = as.numeric(sapply(strsplit(depth, "[-]"), "[[", 1)),
         lyr_bot = as.numeric(sapply(strsplit(depth, "[-]"), "[[", 2)),
         year = as.numeric(sapply(strsplit(Probe, "_"), "[[", 3)),
         PMeco = sapply(strsplit(Probe, "_"), "[[", 1)) %>%
  arrange(lyr_bot)

# split by year
sra.frc.mss.long.01.df <- sra.frc.mss.long.df[sra.frc.mss.long.df$year == 2001, ]
sra.frc.mss.long.19.df <- sra.frc.mss.long.df[sra.frc.mss.long.df$year == 2019, ]
```


```{r plot-frc-mass-losses}
# calc loss, drop samples without all weights
sra.frc.mss.lss.df <- sra.frc.mss.wide.df
sra.frc.mss.lss.df$sum <- rowSums(sra.frc.mss.lss.df[ , c("FPOM", "OPOM", "MOM")])
sra.frc.mss.lss.df$loss <- sra.frc.mss.lss.df$wt_g - sra.frc.mss.lss.df$sum
sra.frc.mss.lss.df$loss_pct <- (sra.frc.mss.lss.df$loss / sra.frc.mss.lss.df$wt_g) * 100
  
# summarize losses by PM, ECO
sra.frc.mss.lss.df$PM <- substr(sra.frc.mss.lss.df$Probe, 1, 2)
sra.frc.mss.lss.df$ECO <- factor(substr(sra.frc.mss.lss.df$Probe, 3, 4), 
                                 levels = c("pp", "wf", "rf"))
sra.frc.mss.lss.df$year <- as.numeric(substr(sra.frc.mss.lss.df$Probe, 11, 14))
sra.frc.mss.lss.df$depth <- sapply(
  strsplit(sra.frc.mss.lss.df$Probe, "_(?!.*_)", perl = TRUE), 
  "[[", 2)
sra.frc.mss.lss.df$lyr_bot <- as.numeric(ifelse(nchar(sra.frc.mss.lss.df$depth) == 3, substr(sra.frc.mss.lss.df$depth, 3, 3), ifelse(nchar(sra.frc.mss.lss.df$depth) == 4, substr(sra.frc.mss.lss.df$depth, 3, 4), substr(sra.frc.mss.lss.df$depth, 4, 5))))

## summarize
# summary(lm(loss_pct ~ PM + ECO + year + lyr_bot, 
#            sra.frc.mss.lss.df[-which(sra.frc.mss.lss.df$Probe == "BSpp_comp_2001_18-28"), ]))
summary(lm(loss_pct ~ PM + ECO, 
           sra.frc.mss.lss.df[-which(sra.frc.mss.lss.df$Probe == "BSpp_comp_2001_18-28"), ]))

# plot
sra.frc.mss.lss.df %>%
  rename(`Mass loss (%)` = loss_pct) %>%
  mutate(eco = factor(ifelse(ECO == "pp", "warm",
                             ifelse(ECO == "wf", "cool", "cold")),
                      levels = c("warm", "cool", "cold")),
         pm = ifelse(PM == "AN", "andesite",
                     ifelse(PM == "BS", "basalt", "granite"))) %>%
  ggplot(., aes(ECO, `Mass loss (%)`, color = pm, shape = eco)) +
  geom_hline(yintercept = 0, color = "black") +
  geom_point(size = 3) +
  scale_color_manual(values = c("andesite" = andesite,
                                "basalt" = basalt,
                                "granite" = granite)) +
  scale_shape_manual(values = c("warm" = 15,
                                "cool" = 17,
                                "cold" = 16)) +
  facet_grid(cols = vars(year)) +
  theme_bw() +
  theme(panel.grid = element_blank())
```

Interestingly, it appears that AN soils have proportionally LESS minC by mass than do GR or BS soils, significantly so at depth. Why would this be? Possibly because AN soils have higher losses (DOC) during fractionation?

## External data
### Atmosphere

```{r atm14c-data}
Datm <- rbind(graven, future14C)
atm.14c <- data.frame(year = Datm[Datm$Date > 2000, "Date"],
                      d14c = Datm[Datm$Date > 2000, "NHc14"],
                      Type = "atmosphere")
```

### from C. Rasmussen ('01 C, N; '09 14C, frc mass, C, N)

```{r load-ras-01-09-data}
# Rasmussen 2001 data
## 2001 summary data
soc.2001 <- data.frame(read_excel("../data/external/sra_ras_sum/sierra_data_summary_2020.xlsx",
                                  sheet = "2001_bulk_data"))

# Rasmussen 2009 data
## 2009 fraction C, N, mass data
sra.09.frc.raw <- read_excel("../data/external/sra_ras_sum/sierra_data_summary_2020.xlsx", sheet = "2009_fraction_data") %>% type.convert(., as.is = TRUE) %>% data.frame

## 2009 summary data
sra.09.sum <- read_excel("../data/external/sra_ras_sum/sierra_data_summary_2020.xlsx", sheet = "Data_Summary_2018_paper") %>% type.convert(., as.is = TRUE) %>% data.frame

## 2009 bulk C
soc.09.blkC.df <- read_excel("../data/external/sra_ras_sum/sierra_data_summary_2020.xlsx", sheet = "2009_bulk_data") %>% type.convert(., as.is = TRUE) %>% data.frame

# Rasmussen 2009 14C data: ISRaD
## read ISRaD fx
sra.09.israd <- ISRaD.read.entry("../data/external/sra_ras_ISRaD/Rasmussen_2018.xlsx")

# get lyr data
sra.09.lyr.df <- sra.09.israd$Rasmussen_2018$layer

# get frc data
sra.09.frc.df <- merge(
  sra.09.israd$Rasmussen_2018$fraction,
  sra.09.israd$Rasmussen_2018$layer[, c("pro_name", "lyr_name", "lyr_bot", "lyr_top", "lyr_c_org", "lyr_n_tot", "lyr_soc", "lyr_bd_samp", "lyr_obs_date_y")], by = c("pro_name", "lyr_name"))

## fill missing d14c data
ix <- which(is.na(sra.09.frc.df$frc_14c) & !is.na(sra.09.frc.df$frc_fraction_modern))
sra.09.frc.df[ix, "frc_14c"] <- convert_fm_d14c(
  fm = sra.09.frc.df[ix, "frc_fraction_modern"], 
  obs_date_y = sra.09.frc.df[ix, "lyr_obs_date_y"])

## reshape ISRaD data to merge with dens.df
sra.frc.14c <- sra.09.frc.df %>%
  mutate(PMeco = as.character(pro_name),
         PM = substr(PMeco, 1, 2),
         ECO = substr(PMeco, 3, 4),
         pro_name = paste0(PMeco, "_", lyr_obs_date_y),
         frc = ifelse(frc_property == "free light", "fPOM", 
                      ifelse(frc_property == "heavy", "minC", "oPOM")),
         Year = 2009) %>% # note that GR samples were technically collected in 2010...
  select(frc_fraction_modern, frc_fraction_modern_sigma, frc_14c, frc_14c_sigma, frc, PMeco, PM, ECO, Year, pro_name, lyr_top, lyr_bot, lyr_bd_samp) %>% 
  rename(F14C = frc_fraction_modern,
         err = frc_fraction_modern_sigma,
         frc_14c_err = frc_14c_sigma) 
```

# Analysis
## Misc. functions

```{r define-C-spline-fxs}
# depth spline for C percent, where d = vector of layer bottom depths
Cspline.fx <- function(df, var.name, d = c(10, 20, 30)) {
  bind_rows(lapply(split(df, df$pro_name), function(x) {
    depths(x) <- pro_name ~ lyr_top + lyr_bot
    x.mps <- mpspline(x, var.name = var.name, d = t(c(0, d)))
    x.std <- t(x.mps$var.std)
    df <- data.frame(c_pct = x.std, depth = row.names(x.std))
    df$depth <- gsub(" [^ ]*$", "", df$depth)
    return(df[1:length(d), ])
  }), .id = "pro_name")
}

# depth spline for monotonic cumulative C stocks; NB: input must be 2-col df w/ depth (1) and cmtv SOC (2)
SOCspline.fx <- function(x, depths, soc) {
  t0 <- data.frame(lyr_bot = 0)
  t0[[soc]] <- 0
  t0.x <- rbind(t0, x)
  
  # fit monotonic cubic spline
  sp <- spline(t0.x, method = "hyman") 
  
  # convert to class "spline" with smooth.spline fxn
  sp.ss <- smooth.spline(sp, all.knots = TRUE) 
  max.d <- max(x[ , 1])
  if (max.d < 31) {
    max.d <- 31
  }
  
  # predict at given depths (linear beyond last measured depth)
  spp <- predict(sp.ss, depths) 
  df <- data.frame(spp)
  colnames(df) <- c("lyr_bot", "lyr_soc") 
  
  # calculate soc per increment from cmtv values
  for(i in seq_along(df$lyr_bot)) {
    if(i == 1) {
      df$lyr_soc[i] <- df$lyr_soc[i]
    } else {
      df$lyr_soc[i] <- df$lyr_soc[i + 1] - df$lyr_soc[i]
    }
  }
  
  # return 
  df$lyr_bot <- df$lyr_bot + 1
  return(df[-length(df$lyr_soc), ])
}
```

## Bulk C
### 2001
```{r blkc-data-2001}
# Fraction samples combined 0-3 and 3-8 depth increments for BSrf and GRrf
## function for calculating depth-weighted average of first two depth increment C content
d1d2.fx <- function(df) {
  d1d2 <- data.frame(ID = paste(df$PMeco[1], df$pro_rep[1], df$lyr_top[1], df$lyr_bot[2], sep = "_"),
                     PMeco = df$PMeco[1],
                     mass_kgm2 = sum(df$mass_kgm2[1], df$mass_kgm2[2]),
                     c_pct = sum(df$c_pct[1] * ((df$lyr_bot[1] - df$lyr_top[1]) / df$lyr_bot[2]),
                                 df$c_pct[2] * ((df$lyr_bot[2] - df$lyr_top[2]) / df$lyr_bot[2])),
                     lyr_soc_kgm2 = sum(df$lyr_soc_kgm2[1], df$lyr_soc_kgm2[2]),
                     pro_name = df$pro_name[1],
                     lyr_top = df$lyr_top[1],
                     lyr_bot = df$lyr_bot[2])
  return(rbind(d1d2,
               df[3:nrow(df), ]))
}

# Create list
soc.2001.ls <- lapply(split(soc.2001, soc.2001$PMeco), function(df) {
  
  # remove NAs
  df <- type.convert(df[complete.cases(df), ])
  
  # filter < 36cm, rename c_pct, add mass and soc stock columns
  df <- df %>%
    filter(lyr_bot < 36) %>%
    rename(c_pct = C.) %>%
    mutate(mass_kgm2 = bd.g.cm3 * (lyr_bot - lyr_top) * fine.earth. * .1,
           lyr_soc_kgm2 = mass_kgm2 * c_pct * 10^-2,
           pro_name = paste(PMeco, pro_rep, sep = "_")) %>%
    select(ID, PMeco, mass_kgm2, c_pct, lyr_soc_kgm2, pro_name, lyr_top, lyr_bot)
  
  # combine 0-3, 3-8 cm depths for GRrf, BSrf
  if (df$PMeco[1] == "GRrf" | df$PMeco[1] == "BSrf") {
    df<- bind_rows(lapply(split(df, df$pro_name), d1d2.fx))
  }

  # calculate cmtv soc stocks
  ls <- split(df, df$pro_name)
  ls <- lapply(ls, function(x) {
    x <- x[order(x$lyr_bot), ] # make sure to order data
    x$lyr_soc_cmtv <- NA
    for(i in seq_along(x$lyr_bot)) {
      if(i == 1) {
        x$lyr_soc_cmtv[i] <- x$lyr_soc_kgm2[i]
      } else {
        x$lyr_soc_cmtv[i] <- x$lyr_soc_kgm2[i] + x$lyr_soc_cmtv[i-1] 
      }
    }
    return(x)
  })
  return(unsplit(ls, df$pro_name))
})

# summarize
soc.2001.sum.df <- bind_rows(soc.2001.ls) %>%
  group_by(PMeco, lyr_top, lyr_bot) %>%
  summarize(across(c(mass_kgm2, c_pct, lyr_soc_kgm2, lyr_soc_cmtv), 
            .fns = list(mean = mean, sd = sd)), .groups = "drop") %>%
  mutate(ID2 = paste0(PMeco, "_", lyr_top, "-", lyr_bot))
```

### 2009
```{r c-data-2009}
# calculate cumulative soc stocks for '09 data
sra.09.lyr.C <- unsplit(lapply(
  split(sra.09.frc.df, sra.09.frc.df$pro_name), function(x) {
    x <- x[order(x$lyr_bot), ] # make sure to order data
    x$lyr_soc_kgm2 <- x$lyr_soc * 10
    x$lyr_soc_cmtv <- NA
    for(i in seq_along(x$lyr_bot)) {
      if(i == 1) {
        x$lyr_soc_cmtv[i] <- x$lyr_soc_kgm2[i]
      } else {
        x$lyr_soc_cmtv[i] <- x$lyr_soc_kgm2[i] + x$lyr_soc_cmtv[i-1] 
      }
    }
    return(x)
  }), sra.09.frc.df$pro_name) %>%
  rename(PMeco = pro_name,
         frc_c_pct = frc_c_perc) %>%
  mutate(frc = ifelse(frc_property == "free light", "FPOM", 
                      ifelse(frc_property == "heavy", "MOM", "OPOM")),
         PM = substr(PMeco, 1, 2),
         ECO = factor(substr(PMeco, 3, 4), levels = c("pp", "wf", "rf")),
         mass_c_g = lyr_soc_kgm2 * frc_c_pct * 10^-2)
```

### 2019

```{r load-2019-soc}
load("/Users/jeff/sra-ts/source/sra.19.blkC.RData")
sra.soc.2019.df <- sra.19.blkC %>%
  filter(lyr_bot < 31) %>%
  group_by(PMeco, lyr_bot) %>%
  summarize(c_pct_lyr = mean(C), .groups = "drop") %>%
  mutate(PM = substr(PMeco, 1, 2),
         ECO = substr(PMeco, 3, 4))
```

## Fraction C
### C, CN profiles

```{r plot-c-n-cn}
# plot depth profiles
# plot fx
frc.pro.plot <- function(df, year, fraction, x) {
  quo_x <- sym(x)
  xlab <- ifelse(x == "CN", "CN", paste(x, "(%)"))
  df$ECO <- factor(df$ECO, levels = c("pp", "wf", "rf"))
  df <- df[order(df$lyr_bot), ]
  df$middepth <- df$lyr_top + (df$lyr_bot - df$lyr_top) / 2
  ggplot(df, aes(!! quo_x, middepth, color = PM, shape = ECO)) +
    geom_hline(yintercept = 0) +
    geom_point(size = 3) +
    geom_path() +
    scale_y_reverse(limits = c(30, 0)) +
    scale_x_continuous() +
    scale_color_manual(name = "Parent material",
                       labels = c("AN" = "andesite",
                                  "BS" = "basalt",
                                  "GR" = "granite"),
                       values = c("AN" = andesite, 
                                  "BS" = basalt, 
                                  "GR" = granite)) +
    scale_shape_manual(name = "Climate",
                       labels = c("pp" = "warm",
                                  "rf" = "cold",
                                  "wf" = "cool"),
                       values = c("pp" = 15, 
                                  "rf" = 16, 
                                  "wf" = 17)) +
    xlab(xlab) +
    ylab("Depth (cm)") +
    ggtitle(paste(year, fraction)) +
    theme_bw() +
    theme(panel.grid.minor = element_blank())
}

# Combine profiles for plotting
frc.C.01.plot.ls <- lapply(sra.frc.2001.ls, bind_rows, .id = "fraction")
lapply(seq_along(frc.C.01.plot.ls), function(i) {
  frc.pro.plot(frc.C.01.plot.ls[[i]], 2001, names(frc.C.01.plot.ls)[i], "C")
})

frc.C.19.plot.ls <- lapply(sra.frc.2019.ls, bind_rows, .id = "fraction")
lapply(seq_along(frc.C.19.plot.ls), function(i) {
  frc.pro.plot(frc.C.19.plot.ls[[i]], 2019, names(frc.C.19.plot.ls)[i], "C")
})

# Calculate and plot CN
lapply(seq_along(frc.C.01.plot.ls), function(i) {
  frc.C.01.plot.ls[[i]][["CN"]] <- frc.C.01.plot.ls[[i]][["C"]] / frc.C.01.plot.ls[[i]][["N"]]
  frc.pro.plot(frc.C.01.plot.ls[[i]], 2001, names(frc.C.01.plot.ls)[i], "CN")
})
lapply(seq_along(frc.C.19.plot.ls), function(i) {
  frc.C.19.plot.ls[[i]][["CN"]] <- frc.C.19.plot.ls[[i]][["C"]] / frc.C.19.plot.ls[[i]][["N"]]
  frc.pro.plot(frc.C.19.plot.ls[[i]], 2019, names(frc.C.19.plot.ls)[i], "CN")
})
```

### C distribution

```{r merge-frc-mass-C}
## merge C and mass data
# '01
sra.frc.mss.C.01.df <- merge(
  bind_rows(
    lapply(sra.frc.2001.ls, function(ls) bind_rows(ls, .id = "PMeco")), .id = "frc"),
  sra.frc.mss.long.01.df, by = c("year", "PMeco", "lyr_bot", "lyr_top", "frc")) %>% 
  mutate(ID2 = sub("comp_2001_", x = ID, replacement = ""))

# calculate C weights
sra.frc.mss.C.01.df$mass_c_g <- sra.frc.mss.C.01.df$mass_sum * (sra.frc.mss.C.01.df$mass_pct / 100) * (sra.frc.mss.C.01.df$C / 100)

# calc synthetic lyr_c (as sum of frc c masses) and percent
if (!("lyr_c_mass_syn" %in% names(sra.frc.mss.C.01.df))) {
  sra.frc.mss.C.01.df <- sra.frc.mss.C.01.df %>%
    group_by(year, PMeco, lyr_bot) %>%
    summarize(lyr_c_mass_syn = sum(mass_c_g), .groups = "drop") %>%
    right_join(., sra.frc.mss.C.01.df, by = c("year", "PMeco", "lyr_bot"))
}
sra.frc.mss.C.01.df$frc_c_pct <- sra.frc.mss.C.01.df$mass_c_g / sra.frc.mss.C.01.df$lyr_c_mass_syn

# add lyr C
sra.frc.mss.C.01.df$lyr_c_pct <- unlist(soc.2001.sum.df[
  match(sra.frc.mss.C.01.df$ID2, soc.2001.sum.df$ID2), "c_pct_mean"])

# relevel fraction factor
sra.frc.mss.C.01.df$frc <- factor(sra.frc.mss.C.01.df$frc, 
                                  levels = c("FPOM", "OPOM", "MOM"))

# convert to data.frame
sra.frc.mss.C.01.df <- data.frame(sra.frc.mss.C.01.df)

# add middepth
sra.frc.mss.C.01.df$middepth <- sra.frc.mss.C.01.df$lyr_top +
  (sra.frc.mss.C.01.df$lyr_bot - sra.frc.mss.C.01.df$lyr_top) / 2

## '19
sra.frc.mss.C.19.df <- merge(
  bind_rows(
    lapply(sra.frc.2019.ls, function(ls) bind_rows(ls, .id = "PMeco")), .id = "frc"),
  sra.frc.mss.long.19.df, by = c("year", "PMeco", "lyr_bot", "lyr_top", "frc")) %>% 
  mutate(ID2 = sub("comp_2019_", x = ID, replacement = ""))

# calculate C weights
sra.frc.mss.C.19.df$mass_c_g <- sra.frc.mss.C.19.df$mass_sum *(sra.frc.mss.C.19.df$mass_pct / 100) * (sra.frc.mss.C.19.df$C / 100)

# add lyr C
sra.frc.mss.C.19.df$lyr_c_pct <- unlist(sra.blk.2019.sum.df[
  match(sra.frc.mss.C.19.df$ID2, sra.blk.2019.sum.df$ID2), "C_mean"])

# calc synthetic lyr_c (as sum of frc c masses) and percent
if (!("lyr_c_mass_syn" %in% names(sra.frc.mss.C.19.df))) {
  sra.frc.mss.C.19.df <- sra.frc.mss.C.19.df %>%
    group_by(year, PMeco, lyr_bot) %>%
    summarize(lyr_c_mass_syn = sum(mass_c_g), .groups = "drop") %>%
    right_join(., sra.frc.mss.C.19.df, by = c("year", "PMeco", "lyr_bot"))
}
sra.frc.mss.C.19.df$frc_c_pct <- sra.frc.mss.C.19.df$mass_c_g / sra.frc.mss.C.19.df$lyr_c_mass_syn
sra.frc.mss.C.19.df$ECO <- factor(sra.frc.mss.C.19.df$ECO, levels = c("pp", "wf", "rf"))
```

```{r plot-frcC-pct-fx}
# box plot fx
frc_c_pct.plot.fx <- function(df, fill_var) {
  
  # set fill variable and guides
  quo_fill_var <- sym(fill_var)
  if (fill_var == "PM") {
    fill_vals <- c("AN" = andesite, "BS" = basalt, "GR" = granite)
  } else {
    fill_vals <- c("pp" = warm, "wf" = cool, "rf" = cold)
  }
  
  # plot
 df %>%
  mutate(frc = factor(frc, levels = c("FPOM", "OPOM", "MOM"))) %>%
  ggplot(., aes(frc, frc_c_pct)) +
  geom_boxplot(aes(fill = !! quo_fill_var), position = "dodge") +
  scale_fill_manual(name = NULL,
                    values = fill_vals) +
  facet_grid(cols = vars(depth)) +
  ylab("C partitioning (%)") +
  xlab(NULL) +
  theme_bw() +
  theme(panel.grid = element_blank()) 
}
```

```{r plot-frcC-pct-01}
# 2001
## prep fx
sra.frc.mss.C.01.prep.fx <- function(df) {
  bind_rows(
    lapply(split(df, df$PMeco), function(d) {
      bind_rows(lapply(split(d, d$frc), function(f) {
        f <- f[order(f$lyr_bot), ]
        f$depth <- seq(1, nrow(f))
        return(f)
      }))
    }))
}

## PM
frc_c_pct.plot.fx(sra.frc.mss.C.01.prep.fx(sra.frc.mss.C.01.df), 
                  fill_var = "PM")

## ECO
frc_c_pct.plot.fx(sra.frc.mss.C.01.prep.fx(sra.frc.mss.C.01.df), 
                  fill_var = "ECO")
```

```{r plot-frcC-pct-09}
# 2009
frc_c_pct.plot.fx(sra.frc.mss.C.01.prep.fx(sra.09.lyr.C), "PM")
frc_c_pct.plot.fx(sra.frc.mss.C.01.prep.fx(sra.09.lyr.C), "ECO")
```

```{r plot-frcC-pct-19}
# 2019
frc_c_pct.plot.fx(sra.frc.mss.C.19.df, "PM")
frc_c_pct.plot.fx(sra.frc.mss.C.19.df, "ECO")
```

```{r c-dist-pm-eco}
sra.frc.mss.C.19.df %>%
  mutate(c_mass_wt = mass_c_g / sum(mass_c_g),
         eco = factor(ifelse(ECO == "pp", "WARM",
                             ifelse(ECO == "wf", "COOL", "COLD")),
                      levels = c("WARM", "COOL", "COLD")),
         pm = ifelse(PM == "AN", "andesite",
                     ifelse(PM == "BS", "basalt", "granite"))) %>%
  group_by(pm, eco, frc) %>%
  summarize(frc_c_pct_30 = round(weighted.mean(frc_c_pct, c_mass_wt) * 100, 0),
            .groups = "drop") %>%
  mutate(frc = factor(frc, levels = c("FPOM", "MOM", "OPOM"),
                      labels = c("free light", "heavy", "occluded light"))) %>%
  ggplot(., aes(pm, frc_c_pct_30)) +
  geom_col(aes(fill = pm), position = "dodge") +
  scale_fill_manual(name = NULL,
                    values = c("andesite" = andesite, 
                               "basalt" = basalt, 
                               "granite" = granite)) +
  facet_grid(cols = vars(eco), rows = vars(frc)) +
  ylab("C partitioning (%)") +
  xlab(NULL) +
  theme_bw() +
  theme(panel.grid = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.y = element_text(size = 14),
        axis.text.y = element_text(size = 10),
        strip.text.x = element_text(face = "bold", size = 12),
        strip.text.y = element_text(face = "bold", angle = 0, size = 12),
        strip.background = element_rect(fill = "white", color = "white"),
        legend.position = "bottom",
        legend.text = element_text(size = 12)) 
```

```{r calc-frc-C-distr-err}
# make wide dataframe for 2019 frc data
merge.vars <- c("year", "PM", "ECO", "lyr_bot")
sra.frc.2019.df <- merge(
  merge(bind_rows(sra.frc.2019.ls$FPOM)[, c(merge.vars, "C", "N")],
        bind_rows(sra.frc.2019.ls$OPOM)[, c(merge.vars, "C", "N")],
        by = merge.vars, suffixes = c("_fPOM", "_oPOM")),
  bind_rows(sra.frc.2019.ls$MOM)[, c(merge.vars, "C", "N")], by = merge.vars) %>%
  rename(C_minC = C, N_minC = N)

# fill missing oPOM data
sra.frc.mss.wide.fill.df <- sra.frc.mss.lss.df
sra.frc.mss.wide.fill.df$oPOM <- ifelse(
  is.na(sra.frc.mss.wide.df$OPOM),
  sra.frc.mss.wide.df$wt_g - (sra.frc.mss.wide.df$FPOM + sra.frc.mss.wide.df$MOM),
  sra.frc.mss.wide.df$OPOM)

# calculate mass precentages
sra.frc.mss.wide.fill.df <- sra.frc.mss.wide.fill.df %>%
  mutate(fPOM_mass_frac = FPOM / wt_g,
         oPOM_mass_frac = OPOM / wt_g,
         minC_mass_frac = MOM / wt_g,
         PMeco = paste0(PM, ECO))

# # model
# summary(lm(fPOM_mass_frac ~ PM + ECO, 
#            sra.frc.mss.wide.fill.df[sra.frc.mss.wide.fill.df$lyr_bot == 10 & sra.frc.mss.wide.fill.df$year == 2019,]))
# summary(lm(oPOM_mass_frac ~ PM + ECO, 
#            sra.frc.mss.wide.fill.df[sra.frc.mss.wide.fill.df$lyr_bot == 10 & sra.frc.mss.wide.fill.df$year == 2019,]))
# summary(lm(minC_mass_frac ~ PM + ECO, 
#            sra.frc.mss.wide.fill.df[sra.frc.mss.wide.fill.df$lyr_bot == 10 & sra.frc.mss.wide.fill.df$year == 2019,]))

# plot
sra.frc.mss.fill.plot.df <- sra.frc.mss.wide.fill.df %>%
  pivot_longer(cols = contains("mass_frac"), names_to = "Fraction", values_to = "Mass percent") %>%
  mutate(Fraction = factor(Fraction,
                           levels = c("fPOM_mass_frac", "oPOM_mass_frac", "minC_mass_frac"),
                           labels = c("fPOM_mass_frac" = "fPOM",
                                      "oPOM_mass_frac" = "oPOM", 
                                      "minC_mass_frac" = "minC")))

# 2019
sra.frc.mss.fill.plot.df %>%
  filter(year == 2019) %>%
  ggplot(., aes(PMeco, `Mass percent`, fill = Fraction)) +
  geom_hline(yintercept = 1, color = "black") +
  geom_col() +
  facet_grid(rows = vars(depth)) +
  theme_bw() +
  theme(panel.grid.minor = element_blank())

# 2001 too hard to plot with all depths
sra.frc.mss.fill.plot.df %>%
  filter(year == 2001) %>%
  group_by(PMeco, Fraction) %>%
  summarize(`Mass percent` = mean(`Mass percent`)) %>%
  ggplot(., aes(PMeco, `Mass percent`, fill = Fraction)) +
  geom_hline(yintercept = 1, color = "black") +
  geom_col() +
  theme_bw() +
  theme(panel.grid.minor = element_blank())

# merge w/ frc lists
sra.frc.soc.2001.ls <- lapply(lapply(sra.frc.2001.ls, bind_rows), function(df) {
  merge(df, 
        soc.2001.sum.df %>%
          mutate(PM = substr(PMeco, 1, 2),
                 ECO = substr(PMeco, 3, 4)) %>%
          select(PM, ECO, lyr_bot, lyr_top, c_pct_mean, lyr_soc_kgm2_mean) %>%
          rename(c_pct_lyr = c_pct_mean,
                 lyr_soc = lyr_soc_kgm2_mean), 
        by = c("PM", "ECO", "lyr_bot", "lyr_top")) %>%
    select(-c("ID", "lyr_soc")) %>%
    mutate(year = 2001)
})
sra.frc.soc.2019.ls <- lapply(lapply(sra.frc.2019.ls, bind_rows), function(df) {
  merge(df, sra.soc.2019.df, by = c("PM", "ECO", "lyr_bot")) %>%
    select(-c("ID", "PMeco"))
})

# merge 01, 19
sra.frc.soc.01.19.ls <- lapply(seq_along(sra.frc.soc.2019.ls), function(i) {
  rbind(sra.frc.soc.2019.ls[[i]], sra.frc.soc.2001.ls[[i]])
})
names(sra.frc.soc.01.19.ls) <- names(sra.frc.soc.2019.ls)

# merge mass frc
sra.frc.mss.fill.long.ls <- split(sra.frc.mss.fill.plot.df, sra.frc.mss.fill.plot.df$Fraction)
sra.frc.mss.fill.long.ls <- lapply(sra.frc.mss.fill.long.ls, function(df) {
  df[ , c("PM", "ECO", "year", "lyr_bot", "Mass percent")]
})
sra.frc.soc.ls <- mapply(
  merge,
  sra.frc.soc.01.19.ls,
  sra.frc.mss.fill.long.ls,
  SIMPLIFY = FALSE)

# Calculate C pct of layer, absolute C, stock per fraction
sra.frc.soc.df <- bind_rows(sra.frc.soc.ls, .id = "Fraction")
sra.frc.soc.df$frc_C_lyr <- (sra.frc.soc.df$C * sra.frc.soc.df$`Mass percent`) /
    sra.frc.soc.df$c_pct_lyr
sra.frc.soc.wide.df <- pivot_wider(
  sra.frc.soc.df, 
  id_cols = c("year", "PM", "ECO", "lyr_bot", "c_pct_lyr",), 
  names_from = c("Fraction"), values_from = c("C", "Mass percent", "frc_C_lyr")) %>%
  merge(., sra.frc.mss.wide.fill.df[ , c("PM", "ECO", "lyr_bot", "year", "wt_g")])

# sum gC frc
sra.frc.soc.wide.df$c_pct_lyr_frc <- rowSums(sra.frc.soc.wide.df[, c("frc_C_lyr_FPOM", "frc_C_lyr_OPOM", "frc_C_lyr_MOM")]) * 100
sra.frc.soc.wide.df$C_diff <- sra.frc.soc.wide.df$c_pct_lyr_frc - 100
```

```{r plot-c-loss}
# 2001
# sra.frc.soc.wide.df %>%
#   filter(year == 2001) %>%
#   mutate(PMeco = paste0(PM, ECO)) %>%
#   ggplot(., aes(PMeco, soc_lost)) +
#   geom_col() +
#   facet_grid(rows = vars(lyr_bot)) +
#   theme_bw() +
#   theme(panel.grid.minor = element_blank())

# 2019
sra.frc.soc.wide.df %>%
  filter(year == 2019) %>%
  mutate(Site = factor(paste0(PM, ECO), 
                       levels = c("ANpp", "ANwf", "ANrf", 
                                  "BSpp", "BSwf", "BSrf", 
                                  "GRpp", "GRwf", "GRrf")),
         `C dif. (% of total stock)` = C_diff) %>%
  ggplot(., aes(Site, `C dif. (% of total stock)`)) +
  geom_col() +
  facet_grid(rows = vars(lyr_bot)) +
  theme_bw() +
  theme(panel.grid.minor = element_blank())
```

## Fraction 14C
### Depth profiles

```{r shape-dens14C-df}
# function for splitting sample names and extracting values from ams list
uScoreSplit.fx <- function(df, ix) sapply(strsplit(df[["Probe"]], "_"), "[[", ix)

# create df
dens.df <- bind_rows(
  lapply(
    ams_results_ls[-grep("thml", names(ams_results_ls))], function(ls) {
      df.ex <- function(x, frc) {
        bind_rows(lapply(x, function(df) {
          df[grep(frc, df$Probe), 2:6]
        }))
      }
      fPOM <- df.ex(ls, "FPOM")
      oPOM <- df.ex(ls, "OPOM")
      minC <- df.ex(ls, "MOM")
      return(
        cbind(rbind(fPOM, oPOM, minC), frc = c(rep("fPOM", nrow(fPOM)),
                                               rep("oPOM", nrow(oPOM)),
                                               rep("minC", nrow(minC)))))
    })) %>%
  mutate(PMeco = uScoreSplit.fx(., 2),
         PM = substr(PMeco, 1, 2),
         ECO = factor(substr(PMeco, 3, 4), levels = c("pp", "wf", "rf")),
         Year = uScoreSplit.fx(., 4),
         pro_name = paste0(PMeco, "_", Year),
         depths = uScoreSplit.fx(., 5),
         lyr_top = as.numeric(sapply(strsplit(depths, "-"), "[[", 1)),
         lyr_bot = as.numeric(sapply(strsplit(depths, "-"), "[[", 2))) %>%
  rename(frc_14c = "∆14C.(‰)",
         frc_14c_err = "err.(‰)") %>%
  select(-c(Probe, depths)) %>%
  filter(!is.na(frc_14c))

# add '09 data
dens.01.09.19.df <- rbind(
  dens.df, 
  sra.frc.14c %>%
    select(-lyr_bd_samp)) %>%
  mutate(pm = ifelse(PM == "AN", "andesite", 
                     ifelse(PM == "BS", "basalt", "granite")),
         eco = factor(ifelse(ECO == "pp", "warm", ifelse(ECO == "wf", "cool", "cold")),
                      levels = c("warm", "cool", "cold")),
         year = as.numeric(Year),
         middepth = lyr_top + (lyr_bot - lyr_top) / 2)
```

``` {r plot-profiles}
# pro plot fx
dens.pro.p.fx <- function(df, year, leg.pos = "right") {
  
  # filter df by year
  df <- df[df$year == year, ]
  
  # get atm 14C
  atm.14c <- atm.14c[atm.14c$year == year + .5, "d14c"]
  
  # set shape to 2001 defaults
  shp.v <- c("warm" = 15, "cool" = 17, "cold" = 16)
  alf <- 1
  lnt.v <- c("2001" = 1)
  stroke <- 1
  ln.sz <- .5
  
  # change shape and alpha as needed
  if (year == 2009) {
    alf <- .6
    lnt.v <- c("2009" = 2)
  } else if (year == 2019) {
    shp.v <- c("warm" = 0, "cool" = 2, "cold" = 1)
    lnt.v <- c("2019" = 3)
    stroke <- 1.2
    ln.sz <- 1
  }
  
  # plot fx
  ggplot(df, aes(frc_14c, lyr_bot, color = frc)) +
    geom_vline(xintercept = atm.14c, color = "gray") +
    geom_point(aes(shape = eco), size = 3, alpha = alf, stroke = stroke) +
    geom_path(aes(linetype = Year), size = ln.sz) +
    scale_color_manual(name = NULL,
                       values = c("minC" = "#9b003f",
                                  "fPOM" = "#3f9b00",
                                  "oPOM" = "#0047af")) +
    scale_linetype_manual(name = NULL, values = lnt.v) +
    scale_shape_manual(name = NULL, values = shp.v) +
    facet_grid(rows = vars(eco), cols = vars(pm)) +
    scale_y_reverse() +
    theme_bw() +
    theme(panel.grid = element_blank(),
          legend.position = leg.pos)
}

# pro plot fx
dens.pro.p.fx2 <- function(df) {
  
  ggplot(df, aes(frc_14c, middepth, color = pm)) +
    geom_vline(xintercept = 0, linetype = 2) +
    geom_point(aes(shape = eco), size = 3) +
    geom_path() +
    scale_color_manual(name = NULL,
                       values = c("andesite" = andesite,
                                  "basalt" = basalt,
                                  "granite" = granite)) +
    scale_shape_manual(name = NULL, 
                       values = c("warm" = 15,
                                  "cool" = 17,
                                  "cold" = 16)) +
    facet_grid(rows = vars(eco), cols = vars(frc2)) +
    scale_y_reverse(limits = c(30, 0)) +
    theme_bw() +
    ylab("Depth (cm)") +
    xlab(expression('Fraction '*Delta*''^14*' (‰)')) +
    guides(color = guide_legend(ncol = 3),
           shape = guide_legend(ncol = 3)) +
    theme_bw() +
    theme(panel.grid = element_blank(),
          axis.title = element_text(size = 14),
          axis.text = element_text(size = 10),
          strip.text.x = element_text(face = "bold", size = 12),
          strip.text.y = element_text(face = "bold", angle = 0, size = 12),
          strip.background = element_rect(fill = "white", color = "white"),
          legend.position = "bottom",
          legend.text = element_text(size = 12)) 
}
```

```{r plot-dens-pros}
# plot profiles by year and fraction type
dens.pro.p.fx(dens.01.09.19.df, 2001)
dens.pro.p.fx(dens.01.09.19.df, 2019)
dens.pro.p.fx(dens.01.09.19.df, 2009)
      
dens.01.09.19.plot.df <- dens.01.09.19.df %>%
  filter(lyr_bot < 31) %>%
  mutate(frc2 = ifelse(frc == "fPOM", "free light", ifelse(frc == "oPOM", "occluded light", "heavy")))
dens.01.09.19.plot.df <- dens.01.09.19.plot.df[dens.01.09.19.plot.df$frc != "oPOM" & dens.01.09.19.plot.df$year == 2019, ]

dens.pro.p.fx2(dens.01.09.19.plot.df)
```

## Spline '01, '09
### Spline C

```{r spline-c-data}
# spline fit fxs for fraction SOC stocks
## Mass preserving spline (quadratic)
mpspline.frc.fx <- function(frc.df) {
  
  # split by frc and PMeco to fit splines to profiles
  lapply(
    split(frc.df, frc.df$frc), function(df) {
      lapply(split(df, df$PMeco), function(x) {
        
        # check for single obs data
        if (nrow(x) > 1) {
          
          # make sure to order data
          x <- x[order(x$lyr_bot), ] 
          
          # calculate cumulative C mass
          x$mass_c_cmtv <- NA
          for(i in seq_along(x$lyr_bot)) {
            if(i == 1) {
              x$mass_c_cmtv[i] <- x$mass_c_g[i]
            } else {
              x$mass_c_cmtv[i] <- x$mass_c_g[i] + x$mass_c_cmtv[i-1] 
            }
          }
          
          # convert to soil profile collection obj and run mpspline
          depths(x) <- PMeco ~ lyr_top + lyr_bot
          x.mps <- suppressMessages(
            mpspline(x, var.name = "mass_c_cmtv", show.progress = FALSE))
          
          # extract 1 cm increment data
          ix <- which(!is.na(x.mps$var.1cm))
          df <- data.frame(middepth = seq(1, length(ix)),
                           lyr_soc = x.mps$var.1cm[ix])
          
          # check for NA values up to 30 cm
          if (length(ix) < 31) {
            # convert to class "spline" with smooth.spline fxn
            sp.ss <- smooth.spline(df)
          
            # predict for NA values up to 31 cm (linear beyond last measured depth)
            std <- seq(0, 30) # in cm 
            sp <- predict(sp.ss, std) 
            df <- data.frame(sp)
            colnames(df) <- c("middepth","lyr_soc") 
          }
          
          # return
          return(df)
        }
      })
    })
}

## depth spline alternative (linear for two increment fits...)
SOCspline.fx <- function(frc.df) {
  
  # split by frc and PMeco to fit splines to profiles
  lapply(
    split(frc.df, frc.df$frc), function(df) {
      lapply(split(df, df$PMeco), function(x) {
    
        # check for single obs data
        if (nrow(x) > 1) {
          
          # make sure to order data
          x <- x[order(x$lyr_bot), ] 
          
          # calculate cumulative C mass
          x$mass_c_cmtv <- NA
          for(i in seq_along(x$lyr_bot)) {
            if(i == 1) {
              x$mass_c_cmtv[i] <- x$mass_c_g[i]
            } else {
              x$mass_c_cmtv[i] <- x$mass_c_g[i] + x$mass_c_cmtv[i-1] 
            }
          }
          
          # fit monotonic cubic spline
          sp <- spline(x[ , c("middepth", "mass_c_cmtv")], method = "hyman") 
          
          # convert to class "spline" with smooth.spline fxn
          sp.ss <- smooth.spline(sp) 
          
          # predict 1 cm increments to 30 cm (linear beyond last measured depth)
          std <- seq(0, 30) # in cm 
          sp <- predict(sp.ss, std) 
          df <- data.frame(sp)
          colnames(df) <- c("middepth","lyr_soc") 
          
          # return 
          return(df)
        }
      })
    })
}
```

```{r run-cspline-frc}
# fraction C stocks
soc.2009.frcSOC.sp <- mpspline.frc.fx(sra.09.lyr.C)
soc.2001.frcSOC.sp <- mpspline.frc.fx(sra.frc.mss.C.01.df)
soc.2001.frcSOC.sp.df <- lapply(soc.2001.frcSOC.sp, function(ls)
  lapply(ls, function(df) {
    # summarize for target intervals
        df <- data.frame(
          lyr_bot = c(10, 20, 30),
          lyr_soc_cmtv = c(df[10, "lyr_soc"],
                           df[20, "lyr_soc"],
                           df[30, "lyr_soc"]))
        df$lyr_soc <- NA
        for (i in seq_along(df$lyr_bot)) {
          if(i == 1) {
              df$lyr_soc[i] <- df$lyr_soc_cmtv[i]
            } else {
              df$lyr_soc[i] <- df$lyr_soc_cmtv[i] - df$lyr_soc_cmtv[i - 1] 
            }
        }
        
        return(df[ , c("lyr_bot", "lyr_soc")])
  }))

test <- sra.frc.mss.C.01.df %>% 
  SOCspline.fx

# Measured SOC as ls
sra.frc.mss.C.01.ls <- lapply(
  split(sra.frc.mss.C.01.df, sra.frc.mss.C.01.df$frc), function(df)
    split(df, df$PMeco))

# make spline lists similar for plotting together
frc.pmeco.fx <- function(soc_ls, name) {
    lapply(seq_along(soc_ls), function(j)
      lapply(seq_along(soc_ls[[j]]), function(i) {
        PMeco <- names(soc_ls[[j]])[i]
        if (!is.data.frame(soc_ls[[j]][[i]])) {
          lyr_soc <- soc_ls[[j]][[i]]
          df <- data.frame(middepth = seq(1, length(lyr_soc)),
                           lyr_soc = lyr_soc)
        } else {
          df <- soc_ls[[j]][[i]]
        }
        # add name and return
        df %>%
          mutate(id = name,
                 PMeco = PMeco,
                 frc = names(soc_ls)[j])
      }))
  }
  
# run fx
ls1 <- frc.pmeco.fx(test, "sp")
ls2 <- frc.pmeco.fx(soc.2001.frcSOC.sp, "mpspline")
  
# combine lists
ls12 <- lapply(seq_along(ls1), function(i)
  bind_rows(
    lapply(seq_along(ls1[[i]]), function(j)
      rbind(ls1[[i]][[j]], ls2[[i]][[j]])
      ), .id = "PMeco") %>%
    mutate(PMeco = factor(PMeco, labels = unique(sra.frc.mss.C.01.df$PMeco))))

# calculate cumulative C mass
sra.frc.mss.C.01.ls2 <- lapply(sra.frc.mss.C.01.ls, function(ls) {
  lapply(ls, function(df) {
    df$mass_c_cmtv <- NA
    for(i in seq_along(df$lyr_bot)) {
      if(i == 1) {
        df$mass_c_cmtv[i] <- df$mass_c_g[i]
      } else {
        df$mass_c_cmtv[i] <- df$mass_c_g[i] + df$mass_c_cmtv[i - 1] 
      }
    }
    return(df)
  })
})

# add additional 0 measurement for step plot
sra.frc.mss.C.01.ls3 <- lapply(sra.frc.mss.C.01.ls2, function(ls)
  bind_rows(lapply(ls, function(df) {
    x <- df[which(df$lyr_bot == min(df$lyr_bot)), ]
    x$lyr_bot <- 0
    rbind(x, df)
  }))
)

# bind rows of sublist
sra.frc.mss.C.01.ls4 <- lapply(sra.frc.mss.C.01.ls2, function(ls) bind_rows(ls))

# add measured data to df12 and plot
p.ls <- lapply(seq_along(ls12), function(i) {
  frc <- ls12[[i]][["frc"]]
  ls12[[i]] %>%
    mutate(PM = substr(PMeco, 1, 2),
           ECO = substr(PMeco, 3, 4)) %>%
    ggplot(., aes(lyr_soc, middepth, color = PM)) +
    geom_step(data = sra.frc.mss.C.01.ls3[[i]],
              aes(mass_c_cmtv, lyr_bot)) +
    geom_path(aes(linetype = id)) +
    geom_point(data = sra.frc.mss.C.01.ls4[[i]],
               aes(mass_c_cmtv, middepth)) +
    scale_color_manual(values = c("AN" = andesite,
                                  "BS" = basalt,
                                  "GR" = granite)) +      
    scale_y_reverse() +
    facet_grid(cols = vars(PM), rows = vars(ECO)) +
    ggtitle(frc) +
    theme_bw() +
    theme(panel.grid = element_blank())
})

# plot
p.ls
```

```{r combine-fpom-opom}
# combine fPOM and oPOM for "POM" fraction
soc.2001.POM.sp.df <- bind_rows(mapply(
  merge,
  soc.2001.frcSOC.sp.df$FPOM,
  soc.2001.frcSOC.sp.df$OPOM,
  MoreArgs = list(by = "lyr_bot", suffixes = c("_fPOM", "_oPOM")),
  SIMPLIFY = FALSE), .id = "PMeco")
soc.2001.POM.sp.df$soc_POM <- rowSums(
  soc.2001.POM.sp.df[ , c("lyr_soc_fPOM", "lyr_soc_oPOM")])
pom.c.01.df <- pivot_longer(
  soc.2001.POM.sp.df, 
  cols = starts_with("lyr_soc"),
  names_to = "frc",
  names_prefix = "lyr_soc_",
  values_to = "soc_frc")
pom.c.01.df$c_POM_frc <- pom.c.01.df$soc_frc / pom.c.01.df$soc_POM
```

### Spline 14C

```{r spline-14C-frc}
# run mpspline on fraction modern data for '01, '09
dens.01.09.df <- dens.01.09.19.df[which(dens.01.09.19.df$year != 2019), ]
dens.01.09.df$frc <- factor(dens.01.09.df$frc, levels = c("fPOM", "oPOM", "minC"))
dens.01.09.ls.sp <- lapply(
  split(dens.01.09.df, dens.01.09.df$frc), function(df) {
    lapply(split(df, df$pro_name), function(x) {
      if (length(which(!is.na(x$F14C))) > 1) {
       depths(x) <- pro_name ~ lyr_top + lyr_bot
        x.mps <- suppressMessages(
          mpspline(x, var.name = "F14C", show.progress = FALSE))
        x.mps$var.1cm <- x.mps$var.1cm[1:30]
        
        # fill NA w/ smooth.spline prediction (linear)
        ix <- which(is.na(x.mps$var.1cm))
        if (length(ix) > 0) {
          sp.ss <- smooth.spline(x.mps$var.1cm[-ix])
          sp <- predict(sp.ss, ix)
          x.mps$var.1cm[ix] <- sp$y
        }
        return(x.mps) 
      }
    })
  })
dens.01.09.ls.sp <- lapply(dens.01.09.ls.sp, function(ls) {
  names(ls) <- ifelse(grepl("2010", names(ls)), sub("2010", "2009", names(ls)), names(ls))
  return(ls)
})
```

```{r plot-frc-fm-splines}
# plot for determining best extrapolation fx
frc.fm.sp.plot.fx <- function(fm.sp.ls) {
  lapply(fm.sp.ls, function(ls) {
    p <- bind_rows(lapply(seq_along(ls), function(i) {
      if (!is.null(ls[[i]])) {
        df <- data.frame(t(do.call(rbind, list(ls[[i]][[1]], ls[[i]][[4]]))))
        df$middepth <- seq(1, length(df[ , 1]))
        df$PM <- substr(df[ , 1], 1, 2)
        df$ECO <- substr(df[ , 1], 3, 4)
        df$Year <- ifelse(substr(df[ , 1], 6, 9) == "2010", "2009",
                          substr(df[ , 1], 6, 9))
        colnames(df)[2] <- "fm"
        df$fm <- as.numeric(as.character(df$fm))
        return(df[ , -1])
      }
    })) %>%
    ggplot(., aes(fm, middepth, color = PM, linetype = Year)) +
    geom_path() +
    scale_color_manual(name = NULL,
                       values = c("AN" = andesite,
                                  "BS" = basalt,
                                  "GR" = granite)) +
    scale_linetype_manual(name = NULL,
                          values = c("2001" = 1,
                                     "2009" = 2)) +
    scale_x_continuous(limits = c(.75, 1.25)) +
    scale_y_reverse() +
    facet_grid(rows = vars(ECO), cols = vars(PM)) +
    theme_bw() +
    theme(panel.grid = element_blank())
  })
}
dens.01.09.ls.sp.p <- frc.fm.sp.plot.fx(dens.01.09.ls.sp)
```

### C-weighted 14C spline

```{r cwt-14c}
# fractions
cwt.frc.fx <- function(soc.ls) {
  lapply(soc.ls, function(pro_ls) {
    lapply(pro_ls, function(soc) {
      d <- seq(10, 30, 10)
      c <- vector(mode = "list", length = length(d))
      for(j in seq_along(d)) {
        if(j == 1) {
          # first depth increment
          c[[j]] <- soc[1:d[j], "lyr_soc"]
        } else {
          # following depth increments
          c[[j]] <- soc[(d[j-1]+1):d[j], "lyr_soc"] 
        }
      }
      return(unlist(lapply(c, function(x) x / sum(x, na.rm = TRUE))))
    })
  })
}

# 2001
cwt.19.01.frc <- cwt.frc.fx(soc.2001.frcSOC.sp)

# 2009
cwt.19.09.frc <- cwt.frc.fx(soc.2009.frcSOC.sp)

# combine
cwt.19.01.09.frc <- lapply(seq_along(cwt.19.01.frc), function(i) {
  ls.01 <- cwt.19.01.frc[[i]]
  names(ls.01) <- paste0(names(cwt.19.01.frc[[i]]), "_2001")
  ls.09 <- cwt.19.09.frc[[i]]
  names(ls.09) <- paste0(names(cwt.19.09.frc[[i]]), "_2009")
  ls <- c(ls.01, ls.09)
  ls[order(names(ls))]
})
names(cwt.19.01.09.frc) <- names(cwt.19.01.frc)

## calculate fm_wts
## '19 depths
fm.wtd.19.01.09 <- lapply(seq_along(dens.01.09.ls.sp), function(i) {
  
  # run fx on non-null elements
  fm.ls <- lapply(seq_along(dens.01.09.ls.sp[[i]]), function(j) {
    
    if (!is.null(dens.01.09.ls.sp[[i]][[j]])) {
      
      # combine cwt, fm
      df <- data.frame(cwt = cwt.19.01.09.frc[[i]][[j]], 
                       fm = dens.01.09.ls.sp[[i]][[j]][["var.1cm"]])
            
      # calculate wtd fm
      df$fm_wt <- df$cwt * df$fm
      
      # summarize for target intervals
      data.frame(
        lyr_bot = c(10, 20, 30),
        fm = c(
          sum(df[1:10, "fm_wt"]), 
          sum(df[11:20, "fm_wt"]), 
          sum(df[21:30, "fm_wt"]))) 
    }
  })
  
  # restore names
  names(fm.ls) <- names(dens.01.09.ls.sp[[i]])
    
  # return wtd fm list
  return(fm.ls)
})
names(fm.wtd.19.01.09) <- names(dens.01.09.ls.sp)
```

```{r prep-cwt-14c-plot}
# make df for splined '01 data
dens.01.09.df <- bind_rows(
  lapply(fm.wtd.19.01.09, function(ls) 
    bind_rows(ls, .id = "PMeco_year")), .id = "frc") %>%
  mutate(PMeco = substr(PMeco_year, 1, 4),
         pm = ifelse(substr(PMeco_year, 1, 2) == "AN", "andesite", 
                     ifelse(substr(PMeco_year, 1, 2) == "BS", "basalt", "granite")), 
         eco = factor(
           ifelse(substr(PMeco_year, 3, 4) == "pp", "warm", 
                  ifelse(substr(PMeco_year, 3, 4) == "wf", "cool", "cold")),
           levels = c("warm", "cool", "cold")),
         year = ifelse(grepl("2001", PMeco_year), 2001, 2009),
         Year = as.character(year),
         frc_14c = convert_fm_d14c(fm = fm, obs_date_y = year, verbose = FALSE),
         frc = factor(frc, levels = c("fPOM", "oPOM", "minC"))) %>%
  rename(F14C = fm) %>%
  select(-PMeco_year)

# combine '01, '19 data; rm NA
dens.01.09.19.sp.df <- rbind(
  dens.01.09.df,
  dens.01.09.19.df[dens.01.09.19.df$year == 2019 & dens.01.09.19.df$lyr_bot < 31, names(dens.01.09.19.df) %in% names(dens.01.09.df)]) %>%
  filter(!is.na(frc_14c))

# add PMeco_depth col
dens.01.09.19.sp.df$PMeco_depth <- paste0(substr(dens.01.09.19.sp.df$PMeco, 1, 4), "_", dens.01.09.19.sp.df$lyr_bot - 10, "-", dens.01.09.19.sp.df$lyr_bot)

# save
save(dens.01.09.19.sp.df, file = "dens.01.09.19.sp.df.RData")

# plot fc
plot.d14c.ts.fx <- function(df, compare_var) {
  
  # set quo_var
  quo_var <- sym(compare_var)
  
  # set facet var and color scales
  if (compare_var == "pm") {
    f_var <- sym("eco")
    cvals <- c("andesite" = andesite, "basalt" = basalt, "granite" = granite) 
  } else {
    f_var <- sym("pm")
    cvals <- c("warm" = warm, "cool" = cool, "cold" = cold)
  }
  
  # split by depth and plot
  lapply(split(df, df$lyr_bot), function(x) {
    x %>%
      filter(frc != "oPOM") %>%
      mutate(ECO = factor(ifelse(eco == "warm", "WARM", ifelse(eco == "cool", "COOL", "COLD")), levels = c("WARM", "COOL", "COLD")),
        frc = ifelse(frc == "fPOM", "free light", "heavy")) %>%
    ggplot(., aes(year, frc_14c, color = !! quo_var)) +
    geom_line(data = atm.14c, aes(year, d14c), 
              linetype = 3, color = "gray", size = .7) +
    geom_line(size = 1) +
    geom_point(aes(shape = ECO), size = 3) +
    scale_color_manual(name = NULL, values = cvals) +
    scale_shape_manual(name = NULL,
                       values = c("WARM" = 15,
                                  "COOL" = 17,
                                  "COLD" = 16),
                       labels = c("WARM" = "warm",
                                  "COOL" = "cool",
                                  "COLD" = "cold")) +
    scale_x_continuous(breaks = c(2001, 2009, 2019)) +
    facet_grid(rows = vars(frc), cols = vars(!! f_var)) +
    ylab(expression('Fraction '*Delta*''^14*' (‰)')) +
    theme_bw() +
    theme(panel.grid = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y = element_text(size = 14),
          axis.text = element_text(size = 10),
          strip.text.x = element_text(face = "bold", size = 12),
          strip.text.y = element_text(face = "bold", angle = 0, size = 12),
          strip.background = element_rect(fill = "white", color = "white"),
          legend.position = "bottom",
          legend.text = element_text(size = 12)) 
  })
}
```

### 14C time series

```{r plot-cwt-14c}
# plot
plot.d14c.ts.fx(dens.01.09.19.sp.df, "pm")
plot.d14c.ts.fx(dens.01.09.19.sp.df, "eco")
```

```{r emmip-plot-fx}
# linear mods for TS
mod.fx <- function(frc, lyr_bot) {
  df <- dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == frc &
                              dens.01.09.19.sp.df$lyr_bot == lyr_bot, ]
  df$year <- df$year - 2000
  lm(frc_14c ~ year * pm * eco, df)
}
mod.ls <- list(
  ts.mod.10.fPOM = mod.fx("fPOM", 10),
  ts.mod.10.minC = mod.fx("minC", 10),
  ts.mod.30.fPOM = mod.fx("fPOM", 30),
  ts.mod.30.minC = mod.fx("minC", 30)
)

 emmip.fx <- function(mod, var) {
   brks <- c(1, 9, 19)
   if (var == "pm") {
     emmip(mod, pm ~ year | eco, CIs = TRUE, at = list(year = brks))$data
   } else {
     emmip(mod, eco ~ year | pm, CIs = TRUE, at = list(year = brks))$data
   }
 }

# return emmip ls
ts.emmip.ls <- lapply(seq_along(mod.ls), function(i) {
  emmip.fx(mod.ls[[i]], "pm") %>%
    mutate(frc = ifelse(i == 1 | 3, "fPOM", "minC"),
           lyr_bot = ifelse(i < 3, 10, 30))
})

# plot fn
plot.fx <- function(ls, data, var) {
  
  # set grouping var
  quo_var <- sym(var)
  
  # set color palette & facet var
  if (var == "pm") {
    cvals <- c("andesite" = andesite, "basalt" = basalt, "granite" = granite)
  } else {
    cvals <- c("warm" = warm, "cool" = cool, "cold" = cold)
  }
  
  # modify point and atm data
  data <- dens.01.09.19.sp.df %>%
    filter(frc != "oPOM") %>%
    mutate(ecoFrc = paste0(eco, " (", frc, ")"),
           year = year - 2000)
  atm.14c$frc <- atm.14c$Type
  
  # run loop
  lapply(seq_along(ls), function(i) {
    
    # get data
    data <- data[which(data$frc == unique(ls[[i]][["frc"]]) &
                         data$lyr_bot == unique(unique(ls[[i]][["lyr_bot"]]))), ]
    
    # get title
    title <- paste(ls[[i]][["frc"]], ls[[i]][["lyr_bot"]])
    
    # plot
    ggplot(ls[[i]], aes(xvar, yvar)) +
      geom_line(aes(color = !! quo_var, linetype = frc), size = .8) +
      geom_ribbon(aes(ymin = LCL, ymax = UCL, fill = !! quo_var, linetype = frc), alpha = .1, show.legend = FALSE) +
      geom_point(data = data,
        aes(year, frc_14c, color = !! quo_var, shape = ecoFrc), size = 1.5, alpha = .8) +
      # atm
      geom_line(data = atm.14c, aes(year, d14c, linetype = frc), color = "black") +
      scale_linetype_manual(name = NULL, 
                            limits = c("minC", "fPOM", "atmosphere"),
                            values = c("minC" = 1,
                                       "fPOM" = 2,
                                       "atmosphere" = 3)) +
      scale_shape_manual(name = NULL,
                         limits = c("warm (minC)", "cool (minC)", "cold (minC)",
                                    "warm (fPOM)", "cool (fPOM)", "cold (fPOM)"),
                         values = c("warm (minC)" = 15, 
                                    "cool (minC)" = 17, 
                                    "cold (minC)" = 16,
                                    "warm (fPOM)" = 0, 
                                    "cool (fPOM)" = 2, 
                                    "cold (fPOM)" = 1)) +  
      scale_color_manual(name = NULL,
                         values = cvals) +
      scale_fill_manual(name = NULL,
                        values = cvals) +
      scale_x_continuous(limits = c(0, 20), 
                         breaks = c(1, 9, 19), 
                         expand = expansion(add = 2),
                         labels = function (x) x + 2000) +
      # coord_cartesian(ylim = c(-110, 170)) +
      facet_grid(rows = vars(eco), cols = vars(pm)) +
      ggtitle(title) +
      theme_bw() +
      theme(panel.grid = element_blank(),
            axis.title = element_blank(),
            axis.text.x = element_text(size = 7),
            strip.background = element_blank(),
            strip.text.x = element_blank(),
            plot.margin = unit(rep(0, 4), "cm"),
            legend.position = "none")
  })
}

ts.plot.ls <- plot.fx(ls = ts.emmip.ls, data = dens.01.09.19.sp.df, var = "pm")

emtrends(lm(frc_14c ~ year * PMeco, dens.01.09.19.sp.df[dens.01.09.19.sp.df$lyr_bot == "10" & dens.01.09.19.sp.df$frc == "fPOM", ]), pairwise ~ PMeco, var = "year")
```

```{r ts-stats}
dens.01.09.19.sp.df$year_i <- dens.01.09.19.sp.df$year - 2000

# fPOM
fPOM.ts.mod.10 <- lm(frc_14c ~ year_i * pm * eco, 
                     dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == "fPOM" &
                                           dens.01.09.19.sp.df$lyr_bot == 10, ])
fPOM.ts.mod.20 <- lm(frc_14c ~ year_i * pm * eco, 
                     dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == "fPOM" &
                                           dens.01.09.19.sp.df$lyr_bot == 20, ])
fPOM.ts.mod.30 <- lm(frc_14c ~ year_i * pm * eco, 
                     dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == "fPOM" &
                                           dens.01.09.19.sp.df$lyr_bot == 30, ])
summary(fPOM.ts.mod.10)
summary(fPOM.ts.mod.20)
summary(fPOM.ts.mod.30) # all ns

# minC
minC.ts.mod.10 <- lm(frc_14c ~ year_i * pm * eco, 
                     dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == "minC" &
                                           dens.01.09.19.sp.df$lyr_bot == 10, ])
minC.ts.mod.20 <- lm(frc_14c ~ year_i * pm * eco, 
                     dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == "minC" &
                                           dens.01.09.19.sp.df$lyr_bot == 20, ])
minC.ts.mod.30 <- lm(frc_14c ~ year_i * pm * eco, 
                     dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc == "minC" &
                                           dens.01.09.19.sp.df$lyr_bot == 30, ])
summary(minC.ts.mod.10)
summary(minC.ts.mod.20)
summary(minC.ts.mod.30) # most interesting

fPOM.ts.mod.10.em <- emmeans(fPOM.ts.mod.10, pairwise ~ pm | eco)
fPOM.ts.mod.10.emip <- emtrends(fPOM.ts.mod.10, pairwise ~ pm * eco, var = "year_i")

fPOM.ts.mod.20.em <- emmeans(fPOM.ts.mod.20, pairwise ~ pm | eco)
fPOM.ts.mod.20.emip <- emtrends(fPOM.ts.mod.20, pairwise ~ pm * eco, var = "year_i") 

minC.ts.mod.10.em <- emmeans(minC.ts.mod.10, pairwise ~ pm | eco)
minC.ts.mod.10.emip <- emtrends(minC.ts.mod.10, pairwise ~ pm * eco, var = "year_i")

minC.ts.mod.20.em <- emmeans(minC.ts.mod.20, pairwise ~ pm | eco)
minC.ts.mod.20.emip <- emtrends(minC.ts.mod.20, pairwise ~ pm * eco, var = "year_i")

minC.ts.mod.30.em <- emmeans(minC.ts.mod.30, pairwise ~ pm | eco)
minC.ts.mod.30.emip <- emtrends(minC.ts.mod.30, pairwise ~ pm * eco, var = "year_i")
```

### Char pool calc

```{r pom-minC-14c}
# Koarashi char equation
char.fx <- function(pom_14c) {
  (pom_14c - 149.9) / -7.278 
}

# make df of pom + minC, calculate char C using Koarashi data
# pom.c.14c.01.df <- merge(
#   pom.c.01.df,
#   dens.01.09.19.sp.df %>%
#     filter(year == 2001),
#   by = c("PMeco", "frc", "lyr_bot")) %>%
#   mutate(wtd_14c = c_POM_frc * frc_14c) %>%
#   group_by(PMeco, pm, eco, lyr_bot, soc_POM, year) %>%
#   summarize(pom_14c = sum(wtd_14c))
# pom.c.14c.01.df <- pom.c.14c.01.df %>%
#   mutate(c_pct_char = char.fx(pom_14c),
#          soc_char = c_pct_char * soc_POM * 10^-2,
#          soc_nonChar = soc_POM - soc_char,
#          d14c_char = -577.9,
#          d14c_nonChar = (pom_14c - d14c_char * c_pct_char) / (100 - c_pct_char))
# ggplot(pom.c.14c.01.df, aes(eco, d14c_nonChar, fill = pm)) +
#   geom_col(position = "dodge") +
#   scale_fill_manual(values = c("andesite" = andesite,
#                                "basalt" = basalt,
#                                "granite" = granite)) +
#   facet_grid(rows = vars(lyr_bot)) +
#   theme_bw() +
#   theme(panel.grid = element_blank())

# # calc. char for oPOM only
# opom.c.14c.01.df <- merge(
#   bind_rows(soc.2001.frcSOC.sp.df$OPOM, .id = "PMeco") %>%
#     mutate(frc = "oPOM"),
#   dens.01.19.df %>%
#     filter(year == 2001),
#   by = c("PMeco", "frc", "lyr_bot")) %>%
#   mutate(c_pct_char = char.fx(frc_14c),
#          soc_char = c_pct_char * lyr_soc * 10^-2,
#          soc_nonChar = lyr_soc - soc_char,
#          d14c_char = -577.9,
#          d14c_nonChar = (frc_14c - d14c_char * c_pct_char) / (100 - c_pct_char))
# ggplot(opom.c.14c.01.df, aes(eco, c_pct_char, fill = pm)) +
#   geom_col(position = "dodge") +
#   scale_fill_manual(values = c("andesite" = andesite,
#                                "basalt" = basalt,
#                                "granite" = granite)) +
#   facet_grid(rows = vars(lyr_bot)) +
#   theme_bw() +
#   theme(panel.grid = element_blank())
```

### Fraction vs respired 14C

```{r plot-LF-inc}
# load data
load("/Users/jeff/sra-ts/source/sra.all.min.RData")
load("/Users/jeff/sra-ts/source/sra.inc.all.RData") # original depths
load("/Users/jeff/sra-ts/source/sra.19.01.inc.RData") # splined
load("/Users/jeff/sra-ts/source/sra.2001.inc.ls.RData") # reps '01
load("/Users/jeff/sra-ts/source/sra.2019.inc.ls.RData") # reps '19

# convert dens.df to wide
dens.01.19.df.w <- dens.01.09.19.df %>%
  select(PMeco, Year, frc, lyr_bot, frc_14c) %>%
  pivot_wider(names_from = frc, values_from = frc_14c)
dens.inc.min.w <- merge(
  sra.inc.all %>%
    filter(d14c_mean > -70),
  dens.01.19.df.w %>%
    mutate(pm = ifelse(grepl("AN", PMeco), "andesite", 
                       ifelse(grepl("BS", PMeco), "basalt", "granite")),
           eco = ifelse(grepl("pp", PMeco), "warm", 
                        ifelse(grepl("wf", PMeco), "cool", "cold"))))

# depth index fx
depth.ix.fx <- function(df, split_var_a = "Year", split_var_b = "PMeco") {
  bind_rows(
    lapply(split(df, df[[split_var_a]]), function(x) 
      bind_rows(lapply(split(x, x[[split_var_b]]), function(y) {
        if (is.null(y$depth)) {
          y[order(y$lyr_bot), ] %>%
            mutate(depth = seq(1, nrow(y))) 
        }
      }))
      )
    )
}

# make reps df
dens.inc.reps.w <- merge(
  bind_rows(
    mapply(rbind, 
           sra.2019.inc.ls,
           sra.2001.inc.ls,
           SIMPLIFY = FALSE)),
  dens.01.19.df.w %>%
    filter(Year != 2009)) %>%
  mutate(pm = ifelse(grepl("AN", PMeco), "andesite", 
                       ifelse(grepl("BS", PMeco), "basalt", "granite")),
         eco = ifelse(grepl("pp", PMeco), "warm", 
                        ifelse(grepl("wf", PMeco), "cool", "cold")),
         year = factor(Year)) %>%
  filter(d14c > -70)
dens.inc.reps.w <- bind_rows(
  lapply(split(dens.inc.reps.w, dens.inc.reps.w$Year), function(x) depth.ix.fx(x, split_var_a = "rep")))

# add depth index col
dens.inc.min.w <- depth.ix.fx(dens.inc.min.w)

# plot inc v LF
# plot profiles by year and fraction type
inc.plot.fx <- function(dens.inc.df, frc) {
  
  quo_var <- sym(frc)
  ylab <- ifelse(frc == "fPOM", expression('free light C '*Delta*''^14*'C (‰)'), 
                 ifelse(frc == "oPOM", 
                        expression('occluded light C '*Delta*''^14*'C (‰)'),
                        expression('min-assoc. C '*Delta*''^14*'C (‰)')))
  dens.inc.df %>%
    filter(d14c_mean > -70) %>%
    ggplot(., aes(d14c_mean, !! quo_var, color = pm, shape = eco)) +
    geom_vline(xintercept = 0, color = "lightgray") +
    geom_hline(yintercept = 0, color = "lightgray") +
    geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
    geom_point(size = 3) +
    geom_errorbarh(aes(xmax = d14c_u, xmin = d14c_l), height = 1) +
    scale_color_manual(name = "Parent material",
                       values = c("andesite" = andesite,
                                  "basalt" = basalt,
                                  "granite" = granite)) +
    scale_shape_manual(name = "Climate",
                       values = c("warm" = 15,
                                  "cool" = 17,
                                  "cold" = 16)) +
    # coord_cartesian(xlim = c(-120, 110), ylim = c(-120, 110)) +
    facet_grid(cols = vars(depth), rows = vars(Year)) + 
    xlab(expression(Delta*''^14*'C-CO'[2]*' (‰)')) +
    ylab(ylab) +
    theme_bw() +
    theme(panel.grid = element_blank(),
          aspect.ratio = 1)
}

# plot respired against nonChar; splined data; '01 only
dens.inc.nonChar.df <- merge(
  pom.c.14c.01.df[ , c("PMeco", "pm", "eco", "lyr_bot", "year", "d14c_nonChar")],
  sra.19.01.inc[ , which(!(names(sra.19.01.inc) %in% c("PM", "ECO")))])
  
dens.inc.nonChar.df %>%
  filter(d14c > -70) %>%
  ggplot(., aes(d14c, d14c_nonChar, color = pm)) +
  geom_vline(xintercept = 0, color = "lightgray") +
  geom_hline(yintercept = 0, color = "lightgray") +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
  geom_point(aes(shape = eco), size = 3) +
  geom_errorbarh(aes(xmin = d14c_min, xmax = d14c_max)) +
  scale_color_manual(name = "Parent material",
                     values = c("andesite" = andesite,
                                "basalt" = basalt,
                                "granite" = granite)) +
  scale_shape_manual(name = "Climate",
                     values = c("warm" = 15,
                                "cool" = 17,
                                "cold" = 16)) +
  facet_grid(cols = vars(lyr_bot)) +
  xlab(expression(Delta*''^14*'C-CO'[2]*' (‰)')) +
  ylab(expression('non-char '*Delta*''^14*'C (‰)')) +
  theme_bw() +
  theme(panel.grid = element_blank(),
        aspect.ratio = 1)
```

```{r plot-inc-dens}
inc.plot.fx(dens.inc.min.w, "fPOM") 
inc.plot.fx(dens.inc.min.w, "oPOM")
inc.plot.fx(dens.inc.min.w, "minC")
```

```{r stats-frc-resp}
fPOM.resp.mod <- lm(fPOM ~ d14c * pm * eco, dens.inc.reps.w)
minC.resp.mod <- lm(minC ~ d14c * pm * eco + year, dens.inc.reps.w)
minC.resp.mod.01 <- lm(minC ~ d14c * pm * eco, 
                       dens.inc.reps.w[dens.inc.reps.w$Year == "2001", ])
minC.resp.mod.19 <- lm(minC ~ d14c * pm * eco, 
                       dens.inc.reps.w[dens.inc.reps.w$Year == "2019", ])

summary(fPOM.resp.mod)
summary(minC.resp.mod)

fPOM.resp.em.PMeco <- emmeans(fPOM.resp.mod, pairwise ~ pm | eco)
fPOM.resp.em.ECOpm <- emmeans(fPOM.resp.mod, pairwise ~ eco | pm)
fPOM.resp.emip.eco <- emtrends(fPOM.resp.mod, pairwise ~ eco, var = "d14c") # ns
fPOM.resp.emip.pm <- emtrends(fPOM.resp.mod, pairwise ~ pm, var = "d14c") # ns

minC.resp.em.PMeco <- emmeans(minC.resp.mod, pairwise ~ pm | eco)
minC.resp.em.ECOpm <- emmeans(minC.resp.mod, pairwise ~ eco | pm)
minC.resp.emip.eco <- emtrends(minC.resp.mod, pairwise ~ eco, var = "d14c") 
# cold - warm p = 0.0176, est dif = -.44
minC.resp.emip.pm <- emtrends(minC.resp.mod, pairwise ~ pm, var = "d14c") # ns
```

```{r frc-resp-plots-egu}
# plot fx
dens.inc.plot.fx <- function(df, var, d14c_var, f_var_c = NA, f_var_r = NA, frc_filter = NA, smooth = FALSE, by_group = TRUE) {
  
  # quote vars
  quo_var <- sym(var)
  quo_d14c_var <- sym(d14c_var)
  
  # mutate df
  df <- df %>%
    select(!oPOM) %>%
    pivot_longer(cols = c(fPOM, minC), names_to = "frc", values_to = "frc_14c") %>%
    mutate(frc = ifelse(frc == "fPOM", "free light", "heavy"),
           ecoFrc = paste0(eco, " (", frc, ")"))
  
  # set colors
  if (var == "pm") {
    col_name <- "Parent material"
    cvals <- c("andesite" = andesite, "basalt" = basalt, "granite" = granite) 
  } else {
    col_name <- "Climate"
    cvals <- c("warm" = warm, "cool" = cool, "cold" = cold)
  }
  
  # frc filter
  if (!is.na(frc_filter)) {
    df <- df %>%
      filter(frc == frc_filter)
    if (frc_filter == "free light") {
      shp_vals <- c("warm (free light)" = 0,
                   "cool (free light)" = 2,
                   "cold (free light)" = 1)
    } else {
      shp_vals <- c("warm (heavy)" = 15,
                   "cool (heavy)" = 17,
                   "cold (heavy)" = 16)
    }
  }
  
  if (by_group) {
    p <- ggplot(df, aes(!! quo_d14c_var, frc_14c, color = !! quo_var)) 
  } else {
    p <- ggplot(df, aes(!! quo_d14c_var, frc_14c)) 
  }
  p <- p +
    geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
    scale_color_manual(name = col_name,
                       values = cvals) +
    scale_shape_manual(name = "Climate (fraction)",
                       values = shp_vals) +
    scale_size(name = "Depth",
               breaks = c(2, 3, 4),
               range = c(2, 4),
               labels = c("1", "2", "3")) +
    # coord_cartesian(xlim = c(-140, 190), ylim = c(-140, 190)) +
    ylab(expression('Fraction '*Delta*''^14*' (‰)')) +
    xlab(expression('Respired '*Delta*''^14*'C-CO'[2]*' (‰)')) +
    guides(size = "none",
           shape = guide_legend(override.aes = list(size = 3))) +
    theme_bw() +
    theme(panel.grid = element_blank(),
          aspect.ratio = 1,
          axis.title = element_text(size = 14),
          axis.text = element_text(size = 12),
          strip.text.x = element_text(face = "bold", size = 12),
          strip.text.y = element_text(face = "bold", angle = 0, size = 12),
          strip.background = element_rect(fill = "white", color = "white"),
          legend.text = element_text(size = 12))
  
  if (!is.na(frc_filter)) {
    p + ggtitle(frc_filter)
  }
  
  if (!is.na(f_var_c)) {
    quo_f_var_c <- sym(f_var_c)
    if (is.na(f_var_r)) {
      p <- p + facet_grid(cols = vars(!! quo_f_var_c))
    } else {
      quo_f_var_r <- sym(f_var_r)
      p <- p + facet_grid(cols = vars(!! quo_f_var_c), rows = vars(!! quo_f_var_r)) 
    }
  }
  
  if (smooth) {
    if (by_group) {
      p <- p + 
        geom_smooth(method = lm, formula = y ~ x, se = FALSE, size = 1.2)
    } else {
      p <- p + 
        geom_smooth(method = lm, formula = y ~ x, se = FALSE, size = 1.2, 
                    color = "black")
    }
    p <- p +
      geom_point(aes(shape = ecoFrc, size = depth, color = !! quo_var), alpha = .3)
      if (d14c_var == "d14c_mean") {
        p <- p + 
          geom_errorbarh(aes(xmax = d14c_u, xmin = d14c_l, color = !! quo_var), 
                         height = 1, alpha = .3)
      }
    p +
      guides(shape = guide_legend(override.aes = list(size = 3, alpha = 1)))
  } else {
    p <- p +
      geom_point(aes(shape = ecoFrc, size = depth, color = !! quo_var))
    if (d14c_var == "d14c_mean") {
      p <- p +
        geom_errorbarh(aes(xmax = d14c_u, xmin = d14c_l, color = !! quo_var), 
                       height = 1, alpha = .3)
    }
    p
  }
}
```

```{r plot-frc-inc-egu}
dens.inc.plot.fx(df = dens.inc.min.w, var = "pm", d14c_var = "d14c_mean", frc_filter = "free light")
# dens.inc.plot.fx(df = dens.inc.min.w, var = "pm", d14c_var = "d14c_mean", frc_filter = "free light", smooth = T, by_group = F)
dens.inc.plot.fx(df = dens.inc.min.w, var = "pm", d14c_var = "d14c_mean", frc_filter = "free light", smooth = T, by_group = T)
dens.inc.plot.fx(df = dens.inc.min.w, var = "pm", d14c_var = "d14c_mean", frc_filter = "heavy")
dens.inc.plot.fx(df = dens.inc.min.w, var = "pm", d14c_var = "d14c_mean", frc_filter = "heavy", smooth = T)
dens.inc.plot.fx(df = dens.inc.min.w, var = "eco", d14c_var = "d14c_mean", frc_filter = "heavy", smooth = T)
dens.inc.plot.fx(df = dens.inc.min.w, var = "eco", d14c_var = "d14c_mean", frc_filter = "free light", smooth = T)
```

## Thermal fractions
### C release (thermograms)
```{r thermal-data}
# load thermogram data and combine
ANwf.30.HF_tml <- read.csv("../data/external/sra_thml_14C_stoner/smooth_ANwf MOM_RPO.csv")
BSwf.30.HF_tml <- read.csv("../data/external/sra_thml_14C_stoner/smooth_BSwf 20-30 Frac fix.csv")
GRwf.30.HF_tml <- read.csv("../data/external/sra_thml_14C_stoner/smooth_GRwf MOM_RPO.csv")
HF_tml.df <- cbind(rbind(ANwf.30.HF_tml, BSwf.30.HF_tml, GRwf.30.HF_tml), 
                   site = c(rep("ANwf", nrow(ANwf.30.HF_tml)), 
                            rep("BSwf", nrow(BSwf.30.HF_tml)), 
                            rep("GRwf", nrow(GRwf.30.HF_tml))))
HF_tml.df$PM <- substr(HF_tml.df$site, 1, 2)

# temp cuts df
HF_tml.temps.df <- data.frame(
  PM = rep(c("AN", "BS", "GR"), ea = 5),
  cut = rep(1:5, 3),
  temp = c(140, 245, 340, 390, 495,
           141.5, 246.5, 290, 364, 484,
           143, 248, 293, 367, 487)
)

# plot
ggplot(HF_tml.df, aes(temp, Moving, color = PM)) +
  # geom_vline(aes(xintercept = temp, color = PM),
  #            data = HF_tml.temps.df,
  #            linetype = "dashed", alpha = .5, show.legend = FALSE) +
  geom_line() +
  scale_color_manual(values = c("AN" = andesite,
                                "BS" = basalt,
                                "GR" = granite)) +
  theme_bw() +
  theme(panel.grid = element_blank())
```

### Thermal fraction 14C

```{r tml-14c}
# wrangle 14C data to df
frc.14c.df <- bind_rows(ams_results_ls$`ams_jena_results-frc19-MOM-thml_2022-01-20`) %>% 
  dplyr::slice(1:15) %>%
  mutate(PM = substr(Probe, 1, 2),
         ECO = "wf", 
         thml_frc = as.numeric(substr(Probe, nchar(Probe), nchar(Probe)))) %>%
  # fix Shane's mislabeling of 14C data
  mutate(PM = ifelse(PM == "AN", "GR", ifelse(PM == "GR", "AN", PM))) %>%
  rename(fm = F14C, fm_err = err, d14c = `∆14C.(‰)`, d14c_err = `err.(‰)`) %>%
  select(PM, ECO, thml_frc, d14c, d14c_err, fm, fm_err)

# save
save(frc.14c.df, file = "frc.14c.df")

# plot
ggplot(frc.14c.df, aes(thml_frc, d14c, fill = PM)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("AN" = andesite,
                               "BS" = basalt,
                               "GR" = granite)) + 
  facet_grid(rows = vars(PM)) +
  theme_bw() +
  theme(panel.grid.minor = element_blank())

# step plot
frc.14c.df %>%
  filter(thml_frc == 5) %>%
  mutate(thml_frc = 6) %>%
  rbind(frc.14c.df, .) %>%
  ggplot(., aes(thml_frc, d14c, color = PM)) +
  geom_hline(yintercept = 0, linetype = "dotted") +
  geom_step() +
  scale_color_manual(name = NULL,
                     values = c("AN" = andesite,
                                "BS" = basalt,
                                "GR" = granite),
                     labels = c("AN" = "andesite",
                                "BS" = "basalt",
                                "GR" = "granite")) +
  scale_x_continuous(breaks = seq(1.5, 5.5), labels = as.character(seq(1, 5))) +
  ylab(expression(Delta*''^14*'C (‰)')) +
  xlab("Thermal fraction") +
  theme_bw() +
  theme(panel.grid = element_blank())
```

```{r tml-frc-c}
csv.dir <- "../data/external/sra_thml_14C_stoner"
nms <- list.files(path = csv.dir, pattern = "*.csv")
nms_smooth <- nms[which(grepl("smooth", nms))]
csvlist <- lapply(seq_along(nms_smooth), function(i) {
  read.csv(paste(csv.dir, nms_smooth[i], sep = "/"))
})
names(csvlist) <- sapply(nms_smooth, substr, start = 8, stop = 11, USE.NAMES = FALSE)
# frc.fx <- function(dat, temps_in) {
#   dat <- dat %>%
#     filter(!is.na(CO2_scaled) | !is.na(temp)) %>%
#     select(temp, CO2_scaled) %>%
#     rename(Temp_av = temp,
#            CO2_av = CO2_scaled)
#   
#   # fit spline
#   # set knots
#   maxtemp <- max(dat$Temp_av)
#   temps <- c(temps_in, maxtemp) #To establish upper limit
#   
#   # define spline
#   sp_fun <- splinefun(dat$Temp_av, dat$CO2_av, method = 'fmm')
#   tol <- 1.5e-4 * 2
#   arealist <- list()
#   l <- 1
#   u <- 1
#   
#   # run spline
#   for (i in temps) {
#     if (u == 1){
#       lower <- 100
#       upper <- temps[u]
#       u <- u+1
#     } else {
#       lower <- temps[l]
#       upper <- temps[u]
#       u <- u + 1
#       l <- l + 1
#     }
#     
#     # calc area
#     area <- integrate(sp_fun, lower, upper, subdivisions = 8000, rel.tol = tol)
#     arealist <- c(arealist, area[1])
#   }
#   
#   ### CO2 - modelled fit
#   area_un <- unlist(arealist)
#   totalarea <- integrate(sp_fun, 80, maxtemp, subdivisions = 8000, rel.tol = tol)[1]
#   totalarea_int <- as.numeric(unlist(totalarea))
#   
#   #Calculate total area the right way
#   fractions <- c()
#   for (y in 1:length(temps)) {
#     thistemp <- temps[y]
#     CO2area <- 0
#     if (y == 1) {
#       CO2area_int <- integrate(sp_fun, 80, temps[y], subdivisions = 10000, rel.tol = tol)[1]
#       CO2area <- as.numeric(unlist(CO2area_int))
#       fracproportion <- CO2area / totalarea_int
#       fractions <- c(fractions, fracproportion)
#     } else {
#       templower <- temps[y-1]
#       CO2area_int <- integrate(sp_fun, templower, temps[y], subdivisions = 8000, rel.tol = tol)[1]
#       CO2area <- as.numeric(unlist(CO2area_int))
#       fracproportion <- CO2area / totalarea_int
#       fractions <- c(fractions, fracproportion)
#     }
#   }
#   data.frame(temp_u = temps, c_pct = fractions * 100)
# }
# cfrc.ls <- lapply(seq_along(csvlist), function(i) {
#   frc.fx(csvlist[[i]], 
#          HF_tml.temps.df[grep(substr(names(csvlist)[i], 1, 2), HF_tml.temps.df$PM), "temp"])
# })
# names(cfrc.ls) <- names(csvlist)
# 
# # get C content
# lapply(seq_along(cfrc.ls), function(i) {
#   if (i == 1) {
#     sum(cfrc.ls[[i]][["c_pct"]][5], cfrc.ls[[i]][["c_pct"]][6])
#   } else {
#     cfrc.ls[[i]][["c_pct"]][6]
#   }
# })

# use Shane's functions
source("./utilities/FractionSizes.R")

ANwf.tml.c.dist <- FractionSizes(
  csv = ANwf.30.HF_tml, 
  temps_in = HF_tml.temps.df[HF_tml.temps.df$PM == "AN", "temp"])
BSwf.tml.c.dist <- FractionSizes(
  csv = BSwf.30.HF_tml, 
  temps_in = HF_tml.temps.df[HF_tml.temps.df$PM == "BS", "temp"])
GRwf.tml.c.dist <- FractionSizes(
  csv = GRwf.30.HF_tml, 
  temps_in = HF_tml.temps.df[HF_tml.temps.df$PM == "GR", "temp"])

FmDist.fx <- function(sample, ramp, FmTemp, bulkFm) {
  
  # get length
  len_ramp <- which(ramp$temp == 775)

  ## Grab only relevant data with new RPO smoothing outputs
  ramp <- ramp[1:len_ramp, ]
  ramp$CO2_prop = ramp$Moving / max(ramp$Moving, na.rm = TRUE)

  ## Thermogram
  # Temperature ramp, with intervals for interpolation = reso (default = 0.1)
  ht <- seq(100, 775, by = .1)
  rsp <- spline(ramp$temp, ramp$CO2_prop, xout = ht)
  
  ## Calculate total area under curve
  totAUC <- AUC(ht, rsp$y, method = 'spline', subdivisions = length(ht))
  
  # loop
  Area <- ht
  n <- 1
  cond <- c(rep(TRUE, length(ht) - 1), FALSE)
  for (t in ht) {
    if (cond[n]) {
      Area[n] <- AUC(ht, rsp$y, from = t, to = ht[n + 1], method = 'spline') / totAUC
    } else {
      Area[n] <- AUC(ht, rsp$y, from = t, to = ht[n], method = 'spline') / totAUC
    }
    
    # update counter
    n <- n + 1
  }
  
  # get natural cubic spline fit
  fm_sp_nCub <- spline(y = bulkFm, x = FmTemp, method = 'natural', xout = ht)
  
  # Create data frame with natural cubic spline fit
  df_sp <- data.frame(temp = ht, area = Area, fm_sp_nCub = fm_sp_nCub$y)
  
  # spline fit stats
  NCubstats <- c(
    wqntl = Hmisc::wtd.quantile(
      df_sp$fm_sp_nCub, 
      weights = df_sp$area,
      normwt = TRUE, 
      probs = c(0.1, 0.25, 0.5, 0.75, 0.9)),
    wmean = Hmisc::wtd.mean(
      df_sp$fm_sp_nCub, 
      weight = df_sp$area))

  # get his
  NCubHist <- plotrix::weighted.hist(
    df_sp$fm_sp_nCub, 
    df_sp$area, 
    freq = FALSE,
    main = 'Natural Cubic Spline, Weighted', 
    breaks = seq(from = min(df_sp$fm_sp_nCub), to = max(df_sp$fm_sp_nCub), length.out = 1000), 
    plot = FALSE)
  
  # return
  list(stats = data.frame(sample, NCubstats),
       density = data.frame(sample, 
                            MidFm = seq(from = 0.0, to = 2, length.out = 1000),
                            NCub = c(0, NCubHist$density),
                            NCubDens = density(NCubHist$density, n = 1000)$y),
       area = data.frame(area = df_sp$area, 
                         NCub_Fm = df_sp$fm_sp_nCub,
                         NCub_14c = convert_fm_d14c(fm = df_sp$fm_sp_nCub,
                                                    obs_date_y = 2019, 
                                                    verbose = FALSE)))
}

ANwf.tml.fm.dist <- FmDist.fx(
  "ANwf", 
  ramp = ANwf.30.HF_tml, 
  FmTemp = ANwf.tml.c.dist[[1]][["50th-Temp"]][2:6],
  bulkFm = frc.14c.df[frc.14c.df$PM == "AN", "fm"])

BSwf.tml.fm.dist <- FmDist.fx(
  "BSwf", 
  ramp = BSwf.30.HF_tml, 
  FmTemp = BSwf.tml.c.dist[[1]][["50th-Temp"]][2:6],
  bulkFm = frc.14c.df[frc.14c.df$PM == "BS", "fm"])

GRwf.tml.fm.dist <- FmDist.fx(
  "GRwf", 
  ramp = GRwf.30.HF_tml, 
  FmTemp = GRwf.tml.c.dist[[1]][["50th-Temp"]][2:6],
  bulkFm = frc.14c.df[frc.14c.df$PM == "GR", "fm"])

# combine and plot
xlim <- convert_fm_d14c(
  fm = c(min(ANwf.tml.fm.dist$stats$NCubstats,
             BSwf.tml.fm.dist$stats$NCubstats,
             GRwf.tml.fm.dist$stats$NCubstats) - .04,
         max(ANwf.tml.fm.dist$stats$NCubstats,
             BSwf.tml.fm.dist$stats$NCubstats,
            GRwf.tml.fm.dist$stats$NCubstats) + .04),
  obs_date_y = 2019, verbose = FALSE)
rbind(
  data.frame(PM = "andesite", ANwf.tml.fm.dist$area),
  data.frame(PM = "basalt", BSwf.tml.fm.dist$area),
  data.frame(PM = "granite", GRwf.tml.fm.dist$area)) %>%
  ggplot(.) +
  geom_vline(xintercept = 0, linetype = "dashed") +
  geom_density(aes(x = NCub_14c, y = ..density.., weight = abs(area), 
                   fill = PM, color = PM), alpha = .6) +
  scale_color_manual(name = "Parent material",
                     values = c("andesite" = andesite,
                                "basalt" = basalt,
                                "granite" = granite)) +
  scale_fill_manual(name = "Parent material",
                    values = c("andesite" = andesite,
                               "basalt" = basalt,
                               "granite" = granite)) +
  xlim(xlim) +
  xlab(expression('C-weighted '*Delta*''^14*'C (‰)')) + 
  theme_bw() +
  theme(panel.grid = element_blank())
```
